Senior Data Engineer - Platform Foundation

The Senior Data Engineer - Platform Foundation is a hands‑on, senior‑level contributor embedded in the Foundations squad. You will design, build, and evolve the shared ingestion platform that underpins data delivery across the company. The platform is the product – your job is to make it reliable, extensible, and easy for other teams to adopt. The Foundations squad operates across three pillars: simplifying the overall data platform landscape by reducing complexity and consolidating redundant patterns; enabling structured and unstructured data ingestion at scale; and supporting the exposure of data products to consumers across the organization. You contribute to all three – making architectural decisions, writing production code, and enabling other teams through documentation and hands‑on support. Key Responsibilities Platform Foundation Development Design and implement reusable ingestion components using dlt and dbt‑core, covering both structured and unstructured data sources, handling high‑volume, append‑heavy, and schema‑drifting patterns. Own the Airflow platform end‑to‑end: extend and maintain DAGs and shared operators, handle deployments and version upgrades, and provide hands‑on support to consuming teams. Ensure incremental loading strategies, data quality checks, and lineage metadata are first‑class outputs of every pipeline. Platform Simplification & Architecture Identify and eliminate redundant ingestion patterns across consuming teams, drive standardization onto shared Platform Foundation components. Collaborate with Solution Architects to evolve the platform architecture in response to new data sources and shifting business requirements. Support data product exposure: define and implement governed interfaces that make data reliably accessible to internal consumers. Contribute to Terraform‑managed infrastructure; participate in multi‑cloud (AWS / Azure) deployment patterns. AI Tooling & Developer Productivity Actively use and evaluate AI‑assisted development tools (GitHub Copilot, Claude Code, etc.) to accelerate platform Foundation delivery. Champion AI tooling adoption within the squad; share best practices and guardrails around AI‑generated code review. Explore AI‑powered capabilities (RAG pipelines, LLM‑assisted data cataloguing) for internal platform documentation and self‑service enablement. DevOps & Reliability Maintain and improve CI/CD pipelines (TeamCity, GitHub Actions) for platform Foundation components. Define and enforce observability standards: DAG/Task‑level alerting, SLA tracking. Participate in on‑call rotation for critical ingestion pipelines; drive post‑incident improvements. Team Enablement & Stakeholder Management Produce platform Foundation documentation, runbooks, and enablement materials for consuming squads. Translate ambiguous or moving business requirements into concrete technical designs – comfortable challenging scope when needed. Mentor mid‑level engineers; participate in hiring and technical assessments. Basic Qualifications Bachelor’s degree in Business, Information Systems, Data/Analytics, Computer Science, or related field. Minimum 5 years in data engineering roles, with at least 2 years in a senior / platform‑level position. Proven track record building production ingestion and transformation pipelines at scale. Experience contributing to a shared platform or internal developer tooling consumed by multiple teams. Core Technical Skills Python: idiomatic, testable, production‑grade code – not just scripting. dbt‑core: advanced modelling (custom materializations), testing, documentation, packages. Apache Airflow: DAG design patterns, custom operators, dynamic task mapping, SLA management. Cloud data platforms: comfortable with one or more major cloud warehouses (Snowflake, BigQuery, Databricks, Microsoft Fabric). SQL: complex analytical queries, window functions, query profiling. Git, CI/CD: trunk‑based development, automated testing gates, pipeline‑as‑code. AI & Modern Tooling Daily user of AI coding assistants (Copilot, Claude Code or equivalent). Understands the limits of AI‑generated code – applies rigorous review, not blind trust. Interest in LLM‑powered data tooling (RAG pipelines, Cortex, semantic layers) is a plus. Behavioral Navigates ambiguity: comfortable when requirements shift mid‑sprint; drives clarification rather than waiting; comfortable working with global teams. Platform mindset: builds for reuse, not one‑off solutions. Direct communicator: raises blockers early, documents decisions, challenges assumptions constructively. Learner: actively tracks technology evolution. At Stellantis, we assess candidates based on qualifications, merit, and business needs. We welcome applications from all people without regard to sex, age, ethnicity, nationality, religion, sexual orientation, disability, or any characteristic protected by law. We believe that diverse teams reflect our identity as a global company, enabling us to better address the evolving needs of our customers and care for our future. #J-18808-Ljbffr Stellantis