Cloud Engineering

We are looking for a Cloud Data Engineer (4–5 years experience) to build, maintain, and optimize a scalable and secure data infrastructure. The candidate will leverage AWS for cloud storage/compute, Databricks for high-performance data processing, and Informatica IDMC for data integration, governance, and quality management. Key Responsibilities Data Pipeline & ETL Development: Design and automate scalable data pipelines (ingestion, processing, transformation) using AWS (Glue, Lambda), Databricks (Spark, Delta Lake), and Informatica IDMC.

Storage & Platform Optimization: Manage and optimize data lakes, warehouses, and databases (S3, RDS, Redshift, DynamoDB) for performance and cost efficiency.

Data Integration & Quality: Integrate diverse internal and external data sources using Informatica IDMC to ensure data consistency, lineage, cataloging, and high quality.

Security & Governance: Implement security standards, data encryption, access controls, and data privacy compliance across AWS and Databricks.

Automation & Workflow Management: Automate routine data processing and monitoring using AWS Step Functions, Lambda, Databricks Jobs, and Informatica workflows.

Requirements / Qualifications Experience: 4 to 5 years of hands-on experience in developing, implementing, and maintaining IT data systems.

Tech Stack Expertise:

AWS: S3, Redshift, RDS, DynamoDB, Glue, Lambda, Step Functions.

Databricks: Apache Spark, Delta Lake, Databricks Jobs.

Integration & Governance: Informatica IDMC (CDI, CDQ, Data Cataloging).

Core Skills: Strong SQL, Python/Scala for Spark processing, ETL/ELT pipeline design, and data governance best practices.