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.

The Foundations squad delivers the shared ingestion and transformation backbone consumed by all Stellantis data domains, across three focus areas:

• Data platform simplification — reducing landscape complexity, consolidating redundant pipelines, and standardizing patterns across teams
• Data ingestion — structured and unstructured sources, multi‑cloud, high‑volume, schema‑resilient
• Data product exposure — enabling reliable, governed delivery of data products to internal consumers

• 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

• 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

• Actively use and evaluate AI‑assisted development tools (Git Hub 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

• Maintain and improve CI/CD pipelines (Team City, Git Hub 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

• 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

• Bachelor's degree in Computer Science, Engineering, Mathematics, Information Systems, or a 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

• 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, Big Query, Databricks, Microsoft Fabric)
• SQL: complex analytical queries, window functions, query profiling
• Git, CI/CD: trunk‑based development, automated testing gates, pipeline‑as‑code

• 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