Lead Data Engineer

About the Role Our Data Engineering team builds and operates the data marts and pipelines that power HR and Workforce reporting and analytics. We're looking for a hands-on Lead Data Engineer who can operate independently in a high-visibility, high-ownership role at the center of our HR data platform. This position offers direct exposure to AI initiatives, cross-functional stakeholders, and the opportunity to meaningfully reduce day-to-day operational load for the team. What You’ll Do Build, maintain, and troubleshoot Spark/EMR ETL pipelines feeding HR and Workforce data marts. Monitor and remediate Data Asset Score issues (data quality rules, governance, and PII minimization actions) to keep HR data assets compliant ahead of deadlines. Serve as the primary coordination point between US HR business stakeholders, offshore engineering teams, and platform/infrastructure teams by translating requirements, removing blockers, and providing status updates. Triage and resolve Jira tickets and bugs raised against HR data mart pipelines; write clear runbooks and pipeline documentation. Build semantic layers and Retrieval-Augmented Generation (RAG) pipelines. Integrate REST and/or GraphQL APIs into data workflows. Operate with minimal oversight, escalating only true blockers and proactively identifying risks before they become incidents. What You’ll Bring 8+ years of Data Engineering experience with strong hands‑on expertise in Spark (PySpark/Scala), SQL, and Python . Experience building and operating ETL/ELT pipelines on cloud platforms such as AWS EMR/S3, Databricks, or equivalent , along with workflow orchestration tools like Airflow . Experience owning pipeline operations end‑to‑end, including reading dependency graphs, diagnosing failures, working with on‑call teams, PagerDuty, Splunk, and driving resolutions across multiple teams. Strong understanding of data warehousing, data mart design, data governance, and PII handling best practices. AI‑native mindset with familiarity in LLM capabilities, evaluation frameworks, and the practical application of AI to engineering challenges. Proficiency with GenAI productivity tools such as Claude, Cursor, and Codex to enhance engineering workflows. Strong communication and stakeholder management skills, with the ability to lead and coordinate offshore engineering teams with minimal supervision. #J-18808-Ljbffr