Data engineer

Our client is expanding their Data Engineering function and hiring across multiple levels (3 to 10 years). You will help build and scale their data architecture, data pipelines, data platform governance, and analytics ecosystem. The role includes working closely with business, analytics, engineering, and external partners to enable a strong enterprise-wide data strategy.

Depending on your experience, you will work on designing, developing, or leading key initiatives in data management, data engineering, data governance, and cloud data systems.

What You Will Do

For all levels

Build and maintain scalable data pipelines and workflows.

Assemble large and complex datasets from multiple sources.

Develop and optimize ETL and ELT processes using AWS cloud, Glue, Python, Py Spark, and related tools.

Enable streaming data pipelines using Kafka or similar tools.

Support data analysts and data scientists with reliable and timely data delivery.

Perform sanity testing, track issues, support UAT, and handle compliance activities.

Work with SQL and No SQL databases.

Improve processes through automation and scalable architecture redesign.

Additional for Manager Level (7 to 10 years)

Independently lead data engineering and governance initiatives.

Create and own the Data Technology Roadmap and architecture vision.

Design modern data warehousing solutions for structured and unstructured data.

Manage stakeholders, vendors, and project delivery.

Oversee data governance, data quality, and audit compliance.

Drive performance improvements and innovation across the data platform.

What We Are Looking For

Experience between 3 to 10 years in Data Engineering.

Strong hands-on skills in AWS, Glue, Python, Py Spark, Kafka, SQL, and data modelling.

Experience with data lakes, streaming data, and workflow orchestration tools.

Good understanding of lending or BFSI domain concepts is an advantage.

Exposure to Snowflake or ML platforms like Cortex is a plus.

A self-driven learner who collaborates well across teams.

Preferred Background

Data Engineers from Banking or Financial Services

Candidates working on high scale data platforms

Engineers who are comfortable with both hands-on execution and cross-team collaboration