Data Engineer

As part of our transformation, we are evolving how finance, business and technology collaborate, shifting to lean-agile, user-centric small product-oriented delivery teams (PODs) that deliver integrated, intelligent, scalable solutions, and bring together engineers, product owners, designers, data architects, and domain experts.

Each pod is empowered to own outcomes end-to-end-refining requirements, building solutions, testing, and delivering in iterative increments. We emphasize collaboration over handoffs, working software over documentation alone, and shared accountability for delivery. Engineers contribute not only code, but also to design reviews, backlog refinement, and retrospectives, ensuring decisions are transparent and scalable across pods. We prioritize reusability, automation, and continuous improvement, balancing rapid delivery with long-term maintainability.

The Senior Data Engineer plays a hands-on role within the Platform Pod, ensuring data pipelines, integrations, and services are performant, reliable, and reusable. This role partners closely with Data Architects, Cloud Architects, and application pods to deliver governed, AI / ML-ready data products.

Job Responsibilities / Typical Day in the Role

Design & Build Scalable Data Pipelines

• Lead development of batch and streaming pipelines using AWS-native tools (Glue, Lambda, Step Functions, Kinesis) and modern orchestration frameworks.
• Implement best practices for monitoring, resilience, and cost optimization in high-scale pipelines.
• Collaborate with architects to translate canonical and semantic data models into physical implementations.

Enable Analytics & AI / ML Workflows

Ensure Data Quality & Governance

Mentor & Collaborate Across Pods

Must Have Skills / Requirements

1) Data Engineering Experience with hands-on expertise in AWS services (Glue, Kinesis, Lambda, RDS, DynamoDB, S3) and orchestration tools (Airflow, Step Functions).

a. 7+ years of experience

2) Proven ability to optimize pipelines for both batch and streaming use cases.

a. 7+ years of experience

3) Knowledge of data governance practices, including lineage, validation, and cataloging.

a. 7+ years of experience

Nice to Have Skills / Preferred Requirements

1) Proven ability to optimize pipelines for both batch and streaming use cases.

2) Knowledge of data governance practices, including lineage, validation, and cataloging.

3) Strong collaboration and mentoring skills; ability to influence pods and domains.

Soft Skills :

1) Strong collaboration and mentoring skills; ability to influence pods and domains.

Technology Requirements :

1) Experience with data engineering, with hands-on expertise in AWS services (Glue, Kinesis, Lambda, RDS, DynamoDB, S3) and orchestration tools (Airflow, Step Functions).

2) Strong skills in SQL, Python, PySpark, and scripting for data transformations.

3) Experience working with modern data platforms (Snowflake, Databricks, Redshift, Informatica).

4) Proven ability to optimize pipelines for both batch and streaming use cases.

5) Knowledge of data governance practices, including lineage, validation, and cataloging.

Additional Notes

#LI-NN2

#LI-hybrid

#DICE