We’re looking for a hands-on Data Engineer to design and scale robust data pipelines and cloud data platforms that power analytics, BI, and AI/GenAI use cases.

You’ll work across ETL/ELT, data modeling, cloud migration, and automation to deliver reliable, high-quality data at scale.

This role is ideal if you’ve worked with Informatica, DBT, Redshift, and have experience modernizing legacy data systems to the cloud.

Key Responsibilities

Data Pipeline Development: Design, build, and optimize ELT/ETL pipelines using Informatica IDMC/PowerCenter, DBT, and Kafka for structured and semi-structured data.

Cloud Migration & Modernization: Migrate on-prem data warehouses and ETL workloads to AWS services like Redshift, S3, Glue, and PostgreSQL.

Data Modeling & Quality: Create scalable data models, implement DBT tests, and enforce data quality, lineage, and governance standards.

Automation & DevOps: Automate infrastructure and deployments using Terraform, Git, CI/CD, and scripting in Python/Shell.

Performance Tuning: Optimize SQL, PL/SQL, and data workflows for performance, cost, and reliability.

Collaboration: Partner with business, analytics, and engineering teams to translate requirements into technical solutions and analytics-ready datasets.

Required Skills

ETL/ELT Tools: 5+ years with Informatica PowerCenter/IDMC, DBT, and strong SQL/PL/SQL skills.

Databases: Hands-on with Oracle 10g–13c, PostgreSQL, Amazon Redshift, and MySQL.

Cloud: Practical experience with AWS – S3, Redshift, Glue, and cloud migration projects.

Programming & Scripting: Python, Shell scripting, and automation of data workflows.

Data Warehousing: Solid understanding of data modeling, performance tuning, and data quality frameworks.

DevOps: Experience with Git, BitBucket, Terraform, and CI/CD for data pipelines.