Sr Engineer, Data Analytics Engineering

Modernization & Cloud 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.

What are we looking for?

We’re looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.

Requirements:

• Proven track record of leading and developing high performing, engaged teams.

• 8+ years of experience in data engineering, software engineering, and/or cloud engineering.

• Bachelor’s degree in Data Science, Computer science or related field; Master’s degree preferred.

• Demonstrable hands-on experience with:

  • Cloud data lake architectures: AWS S3, Glue, Lake Formation, Snowflake, or similar.

  • Data lake design patterns: raw, curated, consumption zones; medallion architecture.

  • Data versioning and schema evolution: e.g., Delta Lake, Apache Iceberg.

  • Data governance and cataloging: including any of the following (preferred experience in multiple tools) Unity Catalog, Collibra, Atlan, AWS Glue Data Catalog.

  • Programming: Python and/or SQL (production code, reusable libraries, tests).

  • Pipeline orchestration: Airflow, Step Functions, dbt, or similar.

  • DevOps for data: Terraform/CloudFormation, CI/CD, monitoring, and runbook creation.

• Strong understanding of data modeling, data quality, and secure data onboarding/governance.

• Experience with both batch and real-time data processing.

Core Competencies:

• Systems Thinking — understands interconnected data flows across platforms.

• Builder Mindset — emphasizes automation, reuse, and simplicity.

• Collaboration — works seamlessly across engineering, architecture, analytics, and operations.

• Leadership — mentors others and elevates the ov