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Senior Data Engineer - Platform FoundationStellantis • Auburn, Alabama, United States
Postuler Maintenant
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Senior Data Engineer - Platform Foundation

Stellantis
  • US
    Auburn, Alabama, United States
  • US
    Auburn, Alabama, United States
Postuler Maintenant

À propos

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
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  • Auburn, Alabama, United States

Compétences linguistiques

  • English
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