Data Engineer II

Own the design, development, testing, deployment, and operation of data pipelines and datasets within an assigned domain

Build and maintain scalable ETL/ELT workflows using SQL, Python, AWS services, and big data technologies

Operate and improve data infrastructure, including Redshift clusters, data lake tables, orchestration workflows, monitoring, alerting, and data quality controls

Improve operational reliability by identifying recurring failures, reducing manual intervention, automating recovery steps, and creating clear runbooks

Partner with Data Science, Business Intelligence, Product, Finance, Engineering, Privacy, and Legal stakeholders to translate business and compliance requirements into scalable data solutions

Build and operate conversational, self-service, and agentic analytics data products

Support compliance, privacy, retention, and governance initiatives, including GDPR, DMA, telemetry migration, data access controls, and retention workflows

Contribute to data foundations that support forecasting, experimentation, ML/AI use cases, self-service analytics, and certified business metrics

Implement data validation, lineage, documentation, and operational mechanisms that improve trust and reduce single points of failure

Drive scoped modernization efforts such as pipeline simplification, migration support, Redshift/data lake improvements, automation, and self-service data enablement

Clarify ambiguous requirements, identify data quality or source-of-truth gaps, and escalate broader trade-offs to senior engineers or managers when appropriate

Mentor junior engineers on scoped technical tasks, coding standards, operational practices, and data quality expectations

Participate in on-call and product support for business-critical pipelines and datasets

Own the design and operation of the data foundations that power GenAI, RAG, and agentic analytics within an assigned domain

Build guardrails, validation, and evaluation mechanisms, both automated and human-in-the-loop, that keep AI-generated outputs such as SQL and metrics accurate and reliable

Apply AI coding assistants and agentic development tools to your daily work and share effective patterns with the team to raise overall engineering velocity.