Sr Engineer, Data Analytics Engineering

• Architect and lead migration of legacy SQL/SSIS/ETL pipelines into AWS-native ingestion and integration patterns.

• Design and implement scalable batch, streaming, and event-driven pipelines using services such as S3, Glue, Lambda, Kinesis, DynamoDB, and Step Functions.

• Build resilient data movement frameworks with embedded governance (metadata, lineage, security, quality).

• Contribute to decommissioning efforts by rationalizing and replacing legacy pipeline assets.

Integration & API Engineering

• Develop secure, performant APIs using modern tooling (API Gateway, Lambda, GraphQL, REST).

• Standardize integration patterns for reusable ingestion modules and domain onboarding.

• Partner with Enterprise Architecture to align on API standards, patterns, and best practices.

Automation & Platform Enablement

• Implement infrastructure-as-code using tools like Terraform or CloudFormation.

• Develop CI/CD pipelines promoting automation, repeatability, and quality.

• Contribute to shared libraries, frameworks, and templates that accelerate onboarding of new data sources.

• Drive observability improvements through logging, metrics, tracing, and automated alerting.

Cross-Team Collaboration

• Establish, develop and lead a top performing team of data engineers.

• Collaborate with Lakehouse Engineering, Warehouse Engineering, AI Engineering, and Data Product teams to ensure reliable and timely data availability.

• Work closely with governance and security teams to enforce enterprise data standards.

• Actively develop and drive a culture of engineering excellence, setting the tone through example.

Strategic Influence

• Shape our team’s technical roadmap and modernization approach.

• Contribute to architectural discussions and design reviews.

• Advocate for scalable, maintainable, cloud-native engineering practices across the organization.