Data Engineering Services

Improve efficiency of data pipelines on cloud and operationalize AI platforms

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Build powerful data platforms that deliver faster insights

Analysts and data scientists of large enterprises usually spend over 70% of their time in data processing rather than on analysis and insights generation. Sigmoid’s data engineering services aim to solve data-related challenges by building efficient data pipelines that modernize platforms and enable rapid AI adoption. We help organize and manage your data better, generate faster insights, build predictive systems, and effectively collaborate with the data science teams to extract the highest ROI from your data investments

Data engineering services to strengthen your data and analytics initiatives

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Data Pipelines

Leverage our data warehousing expertise to build efficient data pipelines, enhance query performance, and generate faster insights. Automate data ingestion from diverse sources with our data connectors and low-code, no-code frameworks.

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ML Engineering

Strength your ML model lifecycle management and accelerate time to business value for AI projects. Move from proof-of-concept to production by deploying robust ML models that can effectively scale across brands and geographies.

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Cloud Transformation

Improve business agility and minimize infrastructure costs with cloud transformation. Our experts assess and architect the ideal cloud setup for your business and help in seamless data migration without risking production SLA and quality.

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Effective enterprise data management and governance to reduce downtime and mitigate data risks. Leverage our proven DataOps services to experience high availability, CI-CD pipelines, testing, and monitoring of enterprise data operations.


How data engineering amplifies business value of advanced analytics

Well-defined data engineering processes create a robust foundation for consistently delivering insights at scale. Read our whitepaper to find out how you can build an efficient data engineering team and maximize business value.

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DE enterprise challenges whitepaper

Customer success stories

Automated data ingestion

Automated data ingestion from 10+ retailers with data lake for CPG firm

Built a data lake to capture and automate diverse data from 10+ retailers and ecommerce sites, enabling real-time insights into sales trends for a CPG company.

bfsi trade surveillance

4x faster data pipelines to enhance regulatory compliance for F100 bank

Efficiently streamlined 100 MN rows of asset class and market data daily to reduce processing time and minimize false alerts for a top-3 global investment bank.

adtech cloud migration

65% cost savings with efficient migration to Google Cloud for AdTech firm

Developed a Spark-based ETL framework on GCP to optimize data infrastructure landscape and deliver over $2.5 MN annual cost savings for an AdTech firm.

mlops model improvement

MLOps delivers 90% improvement in model runtime for FMCG company

MLOps solution helped a CPG client scale their pricing and promotions ML models across geographies and reduce the model runtime from 8 days to just 14 hours.

Data engineer at Sigmoid

  • Experienced in different cloud platforms, open-source technologies and full data technology stack
  • Extensive certification across leading data engineering tech stack such as AWS, GCP, Azure, Snowflake, Matillion, Dataiku, and Databricks
  • An investment of $10,000 per data engineer in technology training across six months
  • Cross-skilled and communicates effectively with data scientists to deliver complex ML projects
  • Leverages data engineering best practices and Sigmoid’s agile framework for data
DE technology stack

Explore our other data and analytics offerings

Data strategy

Build a robust analytics roadmap and modernize your data fabric for driving business transformation.

Data science

Get faster actionable business insights using data science, visualization, and AI for a high success rate on your analytics initiatives.


Leverage our pre-built analytics assets and proprietary frameworks to accelerate data-to-value for your business.

Insights and perspectives

Build a strong data foundation with us!

Accelerate your digital transformation journey with robust data engineering practices.


Data engineering implements robust data validation and cleansing processes to improve data quality and consistency. This includes removing duplicates, correcting errors, standardizing data formats, and filling in missing values. Automated data quality checks, regular audits, and monitoring systems help ensure that the data remains accurate, complete, and reliable over time. Additionally, data engineering practices involve setting up data governance frameworks, which define standards and protocols for data handling.

By ensuring the availability of high-quality, well-structured data necessary for effective model training, data engineering plays a crucial role in supporting machine learning model development. It involves building data pipelines that collect, clean, and transform raw data into formats suitable for analysis and modeling. This process includes dealing with missing values, outliers, and inconsistent data formats, which are crucial for training accurate and reliable machine learning models. Data engineering builds a robust data foundation that enables data scientists to focus on fine-tuning algorithms and optimizing model performance.

Integrating data from multiple sources poses challenges such as varying data formats, inconsistent data quality, and differing update frequencies. These challenges can be addressed by standardizing data formats and protocols, implementing robust data transformation and cleaning processes, using data integration tools such as Talend or Informatica that support a wide range of connectors, and ensuring real-time or near-real-time data synchronization. Establishing clear data integration workflows and governance policies also helps manage these complexities effectively.

To overcome data engineering challenges during cloud transformation, companies should start with a thorough assessment of existing infrastructure and processes, followed by strategic planning aligned with business objectives. Prioritizing migration based on risk and complexity, leveraging cloud-native technologies, and ensuring robust data migration and integration strategies are essential. Establishing strong data governance and security measures, investing in skills development, fostering collaboration, and continuously optimizing cloud resources are also crucial.

Modernize data infrastructure with efficient
pipelines for faster AI implementation