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Data Engineering Services

Fewer Broken Pipelines, More Trustworthy Data

Transform Raw Data Into Business Assets

When your data lives across a dozen disconnected systems, every report starts with reconciliation instead of analysis. Our team, with 5+ years of average consultant experience, builds production-grade pipelines and data platforms that consolidate those sources into one reliable place your reporting and operations can depend on.

Cloud data architecture diagram showing data sources flowing through Azure Data Factory, Synapse Analytics, and Databricks into Power BI

What We Deliver

  • ETL Pipeline Development: Automated ETL processes using PySpark, Python, and Azure Data Factory for reliable data movement
  • Data Warehouse Design: Enterprise data warehouses on Azure Synapse, Snowflake, or AWS Redshift optimized for query performance
  • Data Integration: Connect disparate systems (ERP, CRM, operational databases) and consolidate data from multiple sources
  • Data Quality Framework: Implement validation rules, monitoring, and alerting for data accuracy and completeness
  • Performance Optimization: Tune queries, optimize storage, and improve pipeline processing speed

Technologies & Tools

Cloud Platforms

Azure (Synapse, Data Factory, Storage), Microsoft Fabric (OneLake, Data Factory, Lakehouse), AWS (S3, Redshift, Glue)

Processing Frameworks

PySpark, Apache Spark, Python, SQL

Data Warehouses

Azure Synapse Analytics, Snowflake, AWS Redshift

ETL Tools

Azure Data Factory, AWS Glue, Python scripts

Infrastructure & DevOps

Terraform, ARM templates, CloudFormation, Azure DevOps, AWS CodePipeline, Azure Monitor, CloudWatch

Cloud Infrastructure & Migration

Every data platform we build runs on cloud infrastructure we also set up and maintain. We handle migration, environment setup, and cost control as part of the same engagement, so you get a working platform with a hosted, monitored home for it, not pipelines someone else still has to stand up on their own.

  • Cloud Migration: Move on-premises data workloads to Azure or AWS through a phased assessment, proof of concept, migration, and optimization process that minimizes disruption
  • Infrastructure Setup: Configure compute, storage, networking, and security for data workloads following Azure and AWS best practices
  • Cost Optimization: Right-size resources and implement cost controls so cloud spend tracks actual usage, not headroom you're paying for and not using
  • CI/CD for Data Pipelines: Automate pipeline deployments with Azure DevOps or AWS CodePipeline for faster, safer releases
  • Infrastructure as Code: Terraform, ARM templates, and CloudFormation for reproducible, version-controlled environments

Industry Applications

We have hands-on experience building data platforms for:

  • Healthcare: Clinical data integration, claims processing, research data platforms with regulatory compliance
  • FMCG: Supply chain data consolidation, inventory tracking, sales analytics across distribution networks
  • Manufacturing: Production data pipelines, quality metrics tracking, equipment sensor data processing