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. Team & Technology Context 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 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 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 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