Data Engineering Services

Transform Data into Actionable Insights with Seamless Integration and Automation

We specialize in creating data engineering solutions that break down silos and streamline data integration.

By leveraging serverless components on leading cloud platforms, we reduce costs and eliminate manual data handling. 

Our Data Engineering Services

Marketing Use Case

Customized Data Solutions for Your Business

We understand that every business has unique data challenges and opportunities. That’s why our data engineering solutions are tailored to meet your specific needs. Whether you’re looking to optimize your existing data processes, migrate to a new database system, or start from scratch, our experts are here to guide you every step of the way.

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Building Robust Data Infrastructure

Our team is experienced in setting up Linux servers and managing both relational and non-relational databases, specializing in MariaDB and MongoDB. We work with both Platform as a Service (PaaS) components and serverless databases, helping you maximize efficiency while minimizing operational overhead.

Diverse group of business professionals discussing digital and printed charts at a table.
A detailed view of a data engineering workflow on a screen, showcasing a visual ETL tool like Alteryx, representing Witanalytica's customized data engineering and big data solutions.

Versatility Across Cloud Platforms

We are well-versed with the leading cloud service providers – Azure, Google Cloud Platform (GCP), Amazon Web Services (AWS), and Snowflake. Whether you are already using these platforms or considering a migration, we can leverage our familiarity to design and implement the most effective data engineering solutions for your business.

Sales Use Case

Optimizing Data Processing

We leverage our deep knowledge of Python to implement multiprocessing, allowing us to perform multiple data processing tasks simultaneously. This not only speeds up operations but also optimizes resource usage, providing you with faster, more accurate insights.

Sales Use Case
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Data Governance and Management

We understand the importance of data integrity and compliance. Our team is skilled in data governance, ensuring your data lineage is clear, your data is discoverable, and you remain compliant with relevant regulations.

Analytics Strategic Consulting

Seamless Data Integration

Our data engineering services include seamless integration of data across multiple sources, ensuring consistency and reliability. Whether it’s integrating with your existing ERP, CRM, or other business systems, our solutions are designed to provide a unified view of your data.

Analytics Strategic Consulting

Breaking Down business siloes with data engineering

Our data engineering services start by identifying and aligning with your business goals. We understand that each organization has unique data challenges and opportunities, and our approach ensures that our solutions are tailored to meet these specific needs.

With a focus on scalability and efficiency, we build robust data infrastructures that support your business growth. Our expertise spans across various platforms, ensuring seamless integration and high performance.

What Our Customers Say

Your goals, our expertise

We start from your strategic objectives and work our way back to the right mix of solutions and technologies, not the other way round

Our Strategic Approach to Analytics Deployment

Understand Business and Reporting Needs

Our data engineering process begins with a thorough understanding of your business needs and business intelligence (BI) reporting requirements. This step involves identifying the key metrics, refresh frequency, and the specific reports needed to drive decision-making within your organization.

Identify and Access Data Sources

We then identify the right data sources and obtain access to the underlying systems. This involves collaboration with various departments to ensure all relevant data is considered, whether it's from CRM, ERP, web analytics, or other sources.

Extract, Transform, and Load (ETL)

In this step, we extract the data from the identified sources, transform it to fit the required format and structure, and load it into appropriate storage solutions such as databases, data lakes, and data warehouses. Our ETL process is designed to handle both structured and unstructured data, ensuring comprehensive data integration.

Check for Retroactive Updates

We test for retroactive data updates to ensure data integrity. This involves identifying any overlap periods and performing upserts or replacements as necessary to maintain accurate and up-to-date datasets.

Create Data Assets and Products

We focus on creating reusable data assets and products. These are designed to be utilized multiple times across various departments, maximizing their value and utility. This includes creating standardized data sets, dashboards, and other BI tools.

Develop APIs and Web Interfaces

To encourage data consumption, we develop APIs and web interfaces. These interfaces allow for easy access to the data products, whether through reports, API endpoints, files, emails, or other means. This ensures that the data is readily available to all stakeholders, enhancing operational efficiency.

Ongoing Monitoring and Maintenance

Finally, we provide ongoing monitoring and maintenance of the data infrastructure. This includes regular updates, performance optimization, and ensuring compliance with data governance standards. Our goal is to maintain a high-quality and high availability data environment that supports continuous business growth and innovation.

When Do You Need Our Data Engineering Services

When you require efficient data integration, robust infrastructure, and scalable solutions to manage and utilize significant volumes of data.

This enables seamless data access, supports advanced analytics, and drives informed decision-making across the organization.

Why Hire Witanalytica As Your Data Engineering Consulting Company

Our Data Engineering Pricing Models

Icon depicting a stopwatch and documents, representing the Time and Material flexible billing option for data analytics services.

Time and Material

Suited for projects where the scope may vary, this pay-as-you-go option offers the flexibility to adjust requirements as your project evolves. We’ll work with you to estimate the effort involved and ensure transparency and fairness in billing.

Icon illustrating a piggy bank and calendar, signifying the retainer fee model for consistent analytics support.

Retainer Fee

If you have ongoing analytics needs, our retainer service ensures dedicated support for a set number of hours each month. It’s a great way to secure our team’s availability without the commitment of a full-time hire.

Icon featuring a programmer, denoting the dedicated resources model for data analytics services.

Dedicated Resources

For businesses that anticipate a consistent, high-volume workload, we offer the option of dedicated resources. This model provides you with a team or individual fully focused on your data analytics needs for a sustained period, offering stability and deep integration with your operations.


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AWS offers a broad range of data engineering tools designed to cater to various business needs. Key services include AWS Lambda for serverless computing and AWS Glue for efficient ETL processes. AWS Lake Formation helps in creating data lakes, while Amazon Redshift provides powerful data warehousing solutions. These services are known for their scalability, reliability, and integration capabilities, making AWS a preferred choice for diverse industries.

Azure logo


Azure excels in providing enterprise-grade data engineering solutions. It offers Azure Functions for serverless operations and Azure Data Factory for comprehensive ETL processes. Azure Synapse Analytics and Databricks are renowned for handling complex analytics and large-scale data processing. Azure's strength lies in its seamless integration with Microsoft's ecosystem, making it a popular choice for businesses already using Microsoft products.

Google Cloud Platform logo

Google Cloud Platform (GCP)

GCP stands out for its powerful analytics and machine learning capabilities. It offers Google Cloud Functions for serverless computing and Google Cloud Dataflow for streamlined ETL processes. BigQuery, a fully managed data warehouse, is highly valued for its speed and ability to handle large datasets. GCP is particularly popular among digital marketing and advertising companies due to its seamless integration with Google Analytics 4, Looker Studio, and other Google services.

Snowflake logo


Snowflake is renowned for its innovative data warehousing capabilities, offering seamless scalability and high performance. One of its standout features is the ability to write and execute Python code within its platform using Snowflake Worksheets. This enables data engineers to perform complex data transformations and analytics directly within Snowflake, streamlining workflows and enhancing productivity.

Open Source Initiative logo

Open Source

Open Source solutions provide flexible and customizable data engineering tools suitable for various business needs. KNIME and the closed source Alteryx are popular for their user-friendly ETL capabilities, allowing businesses to build complex workflows without extensive coding. DBT (Data Build Tool) is favored for data transformation and modeling, enabling teams to manage and deploy analytics workflows efficiently. These tools offer robust solutions without the need for extensive infrastructure investments, making them ideal for businesses seeking flexibility and scalability.

Frequently Asked Questions (FAQs)

What is data engineering as a service?

Data Engineering as a Service (DEaaS) is a managed service model where data engineering tasks are outsourced to a specialized provider. This approach allows businesses to leverage the expertise of data engineering professionals without the need to build and maintain an in-house team.

What is the role of data engineering in a company?

The role of data engineering in a company involves designing, building, and maintaining data infrastructure to ensure data is accessible, reliable, and ready for analysis. Data engineers streamline data integration, optimize data processing, manage data storage, ensure data quality, and enable efficient data workflows.

This infrastructure supports data-driven decision-making with datasets and data products that can be used and reused across the organization, ensuring the company has curated and reliable sources of truth that facilitate alignment.

How does data engineering enable decision making?

Data engineering enables decision-making by transforming raw data into structured, reliable datasets. It integrates diverse data sources, ensures data quality, and provides real-time processing. This foundation allows for accurate, timely insights, empowering businesses to make informed, data-driven decisions that drive strategic growth and operational efficiency.

What does a data engineering services company do?

A data engineering services company designs, builds, and maintains the data infrastructure needed for collecting, storing, and analyzing data. They integrate data from various sources, ensure data quality and consistency, and implement ETL (Extract, Transform, Load) processes. Additionally, they create scalable data pipelines, optimize data storage solutions, and support advanced analytics and machine learning initiatives, enabling businesses to leverage their data for informed decision-making and strategic growth.

What are some common challenges businesses face when adopting data engineering services?

Common challenges businesses face when adopting data engineering services include:

  1. Data Integration: Combining data from multiple sources can be complex.
  2. Data Quality: Ensuring accuracy, consistency, and completeness of data.
  3. Scalability: Managing growing data volumes efficiently.
  4. Cost Management: Controlling expenses associated with data infrastructure.
  5. Technical Expertise: Finding skilled professionals to implement and manage data solutions.
  6. Data Security: Protecting sensitive information from breaches.
  7. Change Management: Adapting organizational processes to new data systems.

A company should consider contracting data engineering services when it:

    1. Faces complex data integration challenges.
    2. Needs to build or optimize data infrastructure.
    3. Requires expertise in handling large volumes of data.
    4. Wants to automate data processing and analytics workflows.
    5. Struggles with data quality and governance.
    6. Lacks in-house expertise in advanced data technologies.
    7. Aims to enable data-driven decision-making.
    8. Plans to migrate to or leverage cloud-based data solutions.

Deciding between hiring an in-house data engineering team and outsourcing to an agency depends on several factors that align with your company’s strategic direction, budget, and long-term objectives. Here are some considerations based on the information provided:

Advantages of Outsourcing to an Agency:

  1. Diverse Expertise: Agencies typically offer a broader range of skills and expertise, which can be beneficial if your analytics needs are varied or if you require specialized knowledge.

  2. Cross-Industry Experience: Partnering with an agency gives you access to best practices and valuable insights gained from a wide array of industries, which can enrich your data engineering approach.

  3. Flexible Engagement Models: Agencies offer different collaboration models, from pay-as-you-go to dedicated resources, giving you flexibility in how you manage and budget for analytics services.

  4. Scalability: With an agency, you can quickly scale your data engineering and analytics capabilities up or down based on your current needs without the constraints of headcount and recruitment.

  5. High Availability: Agencies prioritize client support and often plan their resources to ensure uninterrupted availability, which can be crucial for ongoing and time-sensitive analytics needs.

Disadvantages of Outsourcing:

  1. Initial Onboarding: Consultants may require time to become familiar with your company and industry. However, this is often mitigated by the agency’s experience and ability to learn quickly.

  2. Internal Resistance: Employees might be hesitant to work with external consultants. It’s essential to have management buy-in and foster a collaborative environment to ensure successful integration of external expertise.

  3. Cost Considerations: While agencies might have a higher hourly cost compared to in-house salaries, they can provide value through flexibility, lack of long-term commitments, and by offering a variety of pricing models to suit different needs.

In conclusion, if you are looking for a broad range of analytics skills, need flexible and scalable support, and want to benefit from cross-industrial experience without the commitment of hiring full-time staff, outsourcing to an agency might be the right choice for you. However, if you prefer to have analytics expertise embedded within your company and are prepared to invest in hiring and training, building an in-house team could be beneficial. It’s recommended to weigh both options carefully and consider reaching out to agencies for quotes to better understand the potential costs and benefits.

Our services mainly cater to mid sized companies that have reached an inflection point where they can no longer effectively manage their operations, sales, marketing using Excel and Google Sheets. We cover the full spectrum of services such as data analytics consulting, data engineering, database administration, business intelligence and data science.

We can take care of the entire process of setting up an effective data analytics architecture from scratch or alternatively, however we can also help with targeted modular services.

For example, some of our customers have reached out to us when they needed to speed up the development and delivery of dashboards. As such, we have helped them with Business Intelligence development while they chose to keep data engineering capabilities in house.

We have also had customers contract us to build all the necessary pipelines and infrastructure to build a data warehouse from scratch.

Our data engineering services stand out due to our commitment to cost-efficiency and long-term value creation. We prioritize the use of open-source and serverless components, ensuring that we provide the most economical solutions without compromising on quality. By leveraging these technologies, we help our clients significantly reduce their operational costs.

Additionally, we focus on setting up robust data assets and products designed for future reuse. Working across various departments, we frequently encounter opportunities to productize and standardize data dictionaries, such as customer, affiliate, and product dictionaries. This approach not only enhances data consistency across the organization but also ensures that different departments are aligned, using the same accurate and up-to-date data.

Our methodology promotes sustainability and scalability, allowing businesses to maximize their data investments and drive more informed decision-making. By choosing our services, you are opting for a partner dedicated to delivering cost-effective, reusable, and high-quality data solutions.

We have worked with Affiliate Marketing, FMCG, Healthcare, Manufacturing, Transportation and Airlines, Logistics, SaaS and IT, Media and Advertising, Telecomm, Retail and Dealership companies.

Absolutely, please navigate to our Case Studies page and browse through the examples we have showcased there.

Our go to BI tools are PowerBI, Tableau and Domo but we also have experience with Looker Studio, QlikSense, MicroStrategy, Sisense, and Tibco Spotfire.

Choosing the right cloud provider depends largely on your industry and the tools you are already using. Here’s a tailored recommendation based on common industry practices:

Advertising and Digital MarketingGoogle Cloud Platform (GCP): Many advertising and digital marketing companies prefer GCP because it integrates seamlessly with Google Analytics 4 (GA4), Looker Studio, and BigQuery. If your business heavily relies on these tools, GCP provides a familiar and powerful ecosystem for your data needs.

E-commerceAmazon Web Services (AWS): AWS offers a wide array of services like Amazon Redshift for data warehousing and AWS Lambda for serverless computing. If your e-commerce platform is already utilizing tools like Amazon SageMaker for machine learning or Amazon RDS for databases, AWS would be a natural fit.

Finance and BankingMicrosoft Azure: Azure excels in providing enterprise-grade security and compliance, making it ideal for finance and banking sectors. If you are using Microsoft products like PowerBI for business intelligence or Azure SQL Database, migrating to Azure ensures seamless integration and robust data governance.

Healthcare: Microsoft Azure or AWS: Both Azure and AWS offer strong compliance with healthcare regulations (HIPAA). Azure is particularly strong if you are using Microsoft products for patient management systems, while AWS offers comprehensive healthcare-specific services and scalability.

Manufacturing and Logistics: AWS or Azure: AWS provides extensive IoT and machine learning capabilities, which are crucial for manufacturing and logistics optimization. Azure is a strong contender if your operations are deeply integrated with Microsoft products and you require advanced analytics through Azure Synapse Analytics.

Technology and SaaS: AWS or GCP: Both AWS and GCP are excellent for technology companies. AWS provides a broad range of services for building and scaling applications, while GCP is ideal if your products leverage Google’s AI and machine learning tools.

Your choice of cloud provider should align with your current toolset and industry-specific needs. Each cloud provider offers unique advantages, and the best choice will depend on your existing technology stack and the specific requirements of your business operations. If you need further personalized guidance, we’re here to help you assess your current setup and make the optimal decision for your cloud migration journey.

Our approach to data engineering development is grounded in a thorough understanding of your business’s key processes and data requirements. Here’s a detailed look at how we ensure robust and reliable data engineering solutions:

  1. Initial Consultation and Understanding Key Processes: We begin with in-depth discussions with business users and stakeholders to grasp the critical business processes that need data support. This involves understanding the key performance indicators (KPIs) and the data required to drive strategic decision-making.
  2. Requirements Gathering from BI and Business Analysts: We collaborate closely with Business Intelligence (BI) teams and business analysts to gather detailed requirements. This helps us identify the necessary data sources, ensuring our solutions align with reporting needs and business objectives.
  3. Data Source Identification and Integration: We identify and connect to the relevant data sources, which can range from databases, APIs, to third-party systems. Special attention is given to API data fetching, where we focus on versioning and maintaining historical data. This ensures that even if APIs do not store historical changes, our data capturing process retains a comprehensive history.
  4. Data Granularity and Aggregation: Our methodology involves bringing in data at the most granular level possible. We then create multiple warehouse layers to aggregate this data appropriately for various reporting frequencies—daily, weekly, and quarterly. This layered approach ensures flexibility and accuracy in reporting.
  5. ETL Development and Automation: We design and develop efficient ETL (Extract, Transform, Load) processes to handle data ingestion and transformation. Our ETL pipelines are built to ensure data is processed accurately and efficiently, ready for analysis and reporting.
  6. Quality Checks and Validation: Rigorous quality checks are performed to validate that the data aligns with the source systems and meets the predefined requirements. We ensure that our engineered data supports the necessary BI and reporting needs accurately.
  7. Logging and Monitoring: To ensure reliability, we implement comprehensive logging and monitoring processes. This proactive approach helps us detect and resolve issues before they impact the business, ensuring that ETLs run smoothly and data is always accurate.
  8. User Training and Documentation: We provide thorough documentation and training to ensure that users can effectively utilize the new data systems. Our goal is to empower your team to leverage the data for actionable insights and informed decision-making.

By prioritizing low-cost, serverless, and open-source solutions whenever possible, we ensure that our data engineering services are not only effective but also cost-efficient. Our focus on creating reusable data assets and products across different departments fosters consistency and operational efficiency, enabling your organization to make data-driven decisions with confidence.

Yes. This is known as writeback capability. There are very few BI tools that are able to do that, especially PowerBI (with PowerApps) and our partners at

However, you might be looking for a web application instead. Here is our guide that highlights the differences.

Let’s talk to see what is the best solution for your needs.

We understand that data privacy and security are paramount. Here’s how we safeguard your data:

  • Ownership and Control: When we develop your data infrastructure, you have complete ownership and control over the architecture, systems, software, and—most critically—the data itself.
  • In-Ecosystem Work: Our team operates within your infrastructure, avoiding any data transfer outside your secured ecosystem, ensuring data residency and sovereignty.
  • Advanced Security Measures:
    • We implement and use SSH keys and VPNs for secure connections.
    • IP whitelisting and OAuth APIs are standard practice for controlled access.
    • Data encryption both in transit and at rest to protect your information.
    • Frequent password rotations to maintain security integrity.
  • Compliance and Standards:
    • Our processes are designed to align with GDPR and CCPA.
    • We readily adapt to meet any specific requirements of your internal security policies.

Apart from implementing widely know best cybersecurity practices, currently we do not have specialized cyber security personnel which is why we recommend and welcome 3rd party or your inhouse resources to regularly test our infrastructures.

Additionally, we understand the importance of accountability and are willing to explore obtaining insurance coverage that addresses potential damages directly attributable to our services. While we are dedicated to the highest standards of excellence and vigilance, we also recognize the necessity of defining liability.

In the unlikely event of damages and unless specifically insured for, our liability is capped at the total amount billed for our services in the preceding six months of our collaboration. This provision is part of our commitment to transparency and mutual trust in our business relationships.

Your data’s security is our top priority, and we commit to maintaining the highest standards of cybersecurity practices.

If you’re considering our data engineering services, know that we prioritize a partnership approach to pricing. We want to ensure that our services align with your goals and provide clear value. Here’s an outline of how our pricing models can work for you:

  1. Time and Material: Suited for projects where the scope may vary, this pay-as-you-go option offers the flexibility to adjust requirements as your project evolves. We’ll work with you to estimate the effort involved and ensure transparency and fairness in billing.

  2. Retainer Fee: If you have ongoing analytics needs, our retainer service ensures dedicated support for a set number of hours each month. It’s a great way to secure our team’s availability without the commitment of a full-time hire.

  3. Dedicated Resource: For businesses that anticipate a consistent, high-volume workload, we offer the option of dedicated resources. This model provides you with a team or individual fully focused on your business intelligence needs for a sustained period, offering stability and deep integration with your operations.

We understand that each business’s needs are unique, and we’re committed to providing a pricing structure that reflects that.

For a detailed quote that’s tailored to your business’s specific data visualization requirements, please don’t hesitate to reach out to us. Our team is ready to discuss your objectives and how we can align our services for the best outcome.

The duration of a data engineering project varies greatly depending on its scope and complexity.

For specific tasks like one table development it can take 1-2 hours.

More comprehensive projects, such as building a data warehouse for multiple departments within the company, from executive to operational levels, may span several months to 2 years

We’re committed to flexibility and try to scale our resources to meet project deadlines as needed. While we’ve shifted focus towards longer-term collaborations, our goal remains to provide tailored, impactful data engineering services that foster enduring partnerships.

Engaging with our data engineering services is a simple and straight forward process, ensuring we align with your business needs every step of the way.

Here’s how it works:

Before Contract Engagement

  1. Initial Consultation: It all starts with booking a meeting where we discuss your business objectives and how our data engineering consulting services can align with your goals. We conduct a detailed discussion to understand your business strategy and objectives. This helps us explain and show how data can be transformed into actionable insights for your organization.

  2. Tailored Proposal: Based on our discovery meeting, we draft a custom proposal outlining the approach, services offered, and the estimated impact on your business.

  3. Contract Finalization: After reviewing the proposal with you and incorporating any feedback, we finalize the terms and sign the contract, setting the stage for our collaboration.

After Contract Engagement

  1. Onboarding & Data Integration: Our team sets up the necessary infrastructure for data ingestion, processing, and reporting, ensuring a smooth start to our partnership.

  2. Solution Implementation: We implement the best bi solution for your needs that can also include setting up infrastructure, integrations, and the development any agreed-upon dashboards.

  3. Training & Capacity Building: To ensure you get the most out of our services, we provide comprehensive training for your staff on the new systems and tools.

  4. Ongoing Support & Optimization: We offer continuous support, including performance monitoring and optimization, to ensure the solutions evolve with your business.

For more detailed information and to get started with transforming your data into strategic assets, contact us for a personalized consultation.

Absolutely, we encourage you to read the Testimonials section on our homepage.

At Witanalytica, staying at the forefront of data engineering is central to our approach. We actively keep ourselves informed through various channels, including industry news, to ensure we’re aware of the latest developments.

Our team personally tests emerging technologies, often being among the first to access new tools and features through early sign-ups and beta programs. We also delve into product releases and updates, ensuring that we understand the capabilities and applications of new technologies. Beyond external research, we dedicate time for internal projects, experimentation, and training.

This hands-on approach allows us to not only stay updated but also to critically evaluate and integrate new technologies and methods into our solutions, ensuring our clients always benefit from cutting-edge analytics.

At Witanalytica, ensuring the accuracy and reliability of our data analysis is foundational to our approach. We adhere to stringent data quality standards, focusing on accuracy, completeness, consistency, and reliability.

Our process involves rigorous data validation techniques to minimize errors. We tackle common data quality challenges, such as incomplete data, duplicates, data integration issues, and outdated information, by implementing best practices like setting mandatory fields, using data cleaning tools, standardizing data across systems, and establishing regular data refresh schedules.

To maintain the highest data quality, we also ensure data security, assign clear accountability and ownership of data sets, and conduct regular data audits. This comprehensive approach not only mitigates the risk of inaccurate insights but also supports informed decision-making and maintains our clients’ trust.

For a deeper dive into our data quality assurance practices and the importance of high-quality data in analytics, feel free to read more in our detailed article: Data Quality: How to Ensure Your Data Analytics Deliver Accurate Insights

After a project’s completion, our support at Witanalytica doesn’t just end. We actively monitor the dashboards for alerts and triggers to ensure continuous, smooth operation.

Maintenance of existing data pipelines takes precedence, ensuring they perform optimally before we embark on developing new ones. We offer flexible support options tailored to your needs — from a fixed maintenance fee to time-and-material billing for any necessary adjustments or maintenance. This approach allows us to swiftly adapt to changes, such as updates in data sources to ensure that your reports remain accurate.

Moreover, we’re committed to the success and adoption of our solutions. We regularly measure and monitor their usage because we believe in delivering not just solutions, but value that is actively utilized and drives results. Our goal is to ensure that the reports we develop are not only technically sound but also widely adopted and impactful.