End-to-End Data Engineering Services
From ETL pipelines to cloud data warehouses and BI dashboards, we deliver complete data solutions that power your analytics and data science initiatives.
Data Pipelines
Build robust data pipelines using industry-leading tools like Informatica IICS, dbt, Fivetran, Matillion, and Airbyte to move and transform data reliably at scale.
- Informatica IICS Implementation
- dbt (Data Build Tool) Transformations
- Fivetran & Airbyte Connectors
- PySpark & Python Pipelines
- Real-time & Batch Processing
Data Engineering Technologies We Leverage
We utilize industry-leading data engineering tools and platforms across cloud, on-premise, and hybrid environments to deliver robust and scalable data solutions.
Cloud Platforms
AWS, Azure & GCP
Build and deploy data infrastructure on leading cloud platforms including AWS, Microsoft Azure, and Google Cloud Platform for scalability and reliability.
Data Warehouses
Snowflake, Redshift & Databricks
Implement modern cloud data warehouses like Snowflake, AWS Redshift, and Databricks Lakehouse for high-performance analytics and data storage.
ETL & Transformation
IICS, dbt, Fivetran & more
Design data pipelines using Informatica IICS, dbt (Data Build Tool), Fivetran, Matillion, and Airbyte for reliable data extraction, transformation, and loading.
Programming & Big Data
Python, PySpark & SQL
Develop custom data solutions using Python, PySpark for distributed processing, and advanced SQL for data manipulation and analytics.
Databases
Oracle, MySQL & SQL Server
Work with on-premise databases including Oracle, MySQL, Microsoft SQL Server, and PostgreSQL for enterprise data management and migration.
Orchestration & DevOps
Airflow, Dagster, Terraform & Git
Orchestrate complex workflows with Apache Airflow and Dagster, manage infrastructure with Terraform, and version control with Git for DataOps practices.
Case Studies
Real-world examples of how we build modern data foundations for AI and Analytics.
Modernizing Retail Analytics with Snowflake & dbt
Slow ETL processes and limited compute capacity meant store managers received inventory reports with day-old data, leading to stockouts and lost revenue.
Read Case StudyPredictive Logistics with Databricks & PySpark
Processing terabytes of telematics data (GPS, speed, fuel) in real-time was impossible with their legacy SQL database.
Read Case StudyEnterprise Oracle to Cloud Migration & Data Mesh
High licensing costs, rigid data structures, and inability to integrate with modern AI tools were stifling innovation.
Read Case Study