Data Engineer

Overview

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, 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

• 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 : Build pipelines that deliver clean, well-structured data to analysts, BI tools, and ML pipelines.
• Work with data scientists to enable feature engineering and deployment of ML models into production environments.
• Ensure Data Quality & Governance : Embed validation, lineage, and anomaly detection into pipelines.
• Contribute to the enterprise data catalog and enforce schema alignment across pods.
• Partner with governance teams to implement role-based access, tagging, and metadata standards.
• Mentor & Collaborate Across Pods : Guide junior data engineers, sharing best practices in pipeline design and coding standards.
• Participate in pod ceremonies (backlog refinement, sprint reviews) and program-level architecture syncs.
• Promote reusable services and reduce fragmentation by advocating platform-first approaches.

Must Have Skills / Requirements

Nice to Have Skills / Preferred Requirements

Soft Skills

Technology Requirements

Additional Notes

Hybrid – 3 days on-site in CA – Burbank.

LI-NN2

LI-hybrid

DICE

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