Data Engineer.

What We Do/Project

As part of the Studio Economics 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
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
1) Experience in data engineering, 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) Strong skills in SQL, Python, PySpark, and scripting for data transformations.
a. 7+ years of experience.
3) Experience working with modern data platforms (Snowflake, Databricks, Redshift, Informatica).
a. 7+ years of experience.

Nice to Have Skills / Preferred Requirements
1) None.

Soft Skills:
1) Strong collaboration and mentoring skills; ability to influence across pods and domains.
2) Knowledge of data governance practices, including lineage, validation, and cataloging.

Technology Requirements:
1) Strong skills in SQL, Python, PySpark, and scripting for data transformations.
2) hands-on expertise in AWS services (Glue, Kinesis, Lambda, RDS, DynamoDB, S3) and orchestration tools (Airflow, Step Functions).
3) Experience working with modern data platforms (Snowflake, Databricks, Redshift, Informatica).
4) Proven ability to optimize pipelines for both batch and streaming use cases.

Education / Certifications
1) None required.

Interview Process / Next Steps
1) 1-2 rounds - Manager and VP