Senior Data Engineer (Foundations)

Stellantis•Auburn Hills, MI

About The Position

The Senior Data Engineer – Platform Foundation is a hands-on, senior-level contributor embedded in the Foundations squad. The role designs, builds, and evolves the shared ingestion platform that supports data delivery across the company, with a focus on reliability, extensibility, and ease of adoption by other teams. The Foundations squad works to simplify the data platform landscape, enable structured and unstructured data ingestion at scale, and support exposure of data products to organizational consumers. The role contributes across all three areas through architectural decisions, production code, documentation, and hands-on enablement. The position develops reusable ingestion components with dlt and dbt-core for varied data sources and high-volume, append-heavy, and schema-drifting workloads. It owns the Airflow platform end-to-end, including DAGs, shared operators, deployments, upgrades, and support for consuming teams. It also establishes incremental loading, data quality checks, and lineage metadata as standard pipeline outputs. The engineer identifies and consolidates redundant ingestion patterns, collaborates with Solution Architects on platform architecture, and helps implement governed interfaces for reliable data-product access. The role contributes to Terraform-managed infrastructure and multi-cloud deployment patterns across AWS and Azure. The position uses and evaluates AI-assisted development tools, promotes their responsible adoption, and explores AI-powered capabilities for internal documentation and self-service. It maintains CI/CD pipelines, improves observability and SLA tracking, participates in the on-call rotation for critical ingestion pipelines, and drives post-incident improvements. It also creates documentation and enablement materials, translates ambiguous business requirements into technical designs, mentors mid-level engineers, and participates in hiring and technical assessments.

Requirements

Bachelor's degree in Business, Information Systems, Data/Analytics, Computer Science, or a related field.
At least 5 years of experience in data engineering roles, including at least 2 years in a senior or platform-level position.
Proven track record building production ingestion and transformation pipelines at scale.
Experience contributing to a shared platform or internal developer tooling used by multiple teams.
Production-grade, idiomatic, testable Python skills.
Advanced dbt-core skills, including modelling, custom materializations, testing, documentation, and packages.
Apache Airflow skills, including DAG design patterns, custom operators, dynamic task mapping, and SLA management.
Comfort with one or more major cloud data warehouses, such as Snowflake, BigQuery, Databricks, or Microsoft Fabric.
SQL skills, including complex analytical queries, window functions, and query profiling.
Git and CI/CD skills, including trunk-based development, automated testing gates, and pipeline-as-code.
Daily use of AI coding assistants such as GitHub Copilot, Claude Code, or equivalent.
Understands the limitations of AI-generated code and applies rigorous review rather than relying on it blindly.

Nice To Haves

Interest in LLM-powered data tooling, including RAG pipelines, Cortex, or semantic layers.

Responsibilities

Design and implement reusable ingestion components using dlt and dbt-core for structured and unstructured sources, including high-volume, append-heavy, and schema-drifting workloads.
Own and maintain the Airflow platform, including DAGs, shared operators, deployments, version upgrades, and hands-on support for consuming teams.
Ensure incremental loading strategies, data quality checks, and lineage metadata are included in pipelines.
Identify and eliminate redundant ingestion patterns and standardize teams on shared Platform Foundation components.
Collaborate with Solution Architects to evolve platform architecture in response to new data sources and changing business requirements.
Define and implement governed interfaces that make data products reliably accessible to internal consumers.
Contribute to Terraform-managed infrastructure and multi-cloud deployment patterns across AWS and Azure.
Use and evaluate AI-assisted development tools to accelerate platform delivery, and champion adoption by sharing best practices and guardrails for reviewing AI-generated code.
Explore AI-powered capabilities such as RAG pipelines and LLM-assisted data cataloguing for internal documentation and self-service enablement.
Maintain and improve CI/CD pipelines using TeamCity and GitHub Actions.
Define and enforce observability standards, including DAG- and task-level alerting and SLA tracking.
Participate in the on-call rotation for critical ingestion pipelines and drive post-incident improvements.
Produce platform documentation, runbooks, and enablement materials for consuming squads.
Translate ambiguous or changing business requirements into technical designs and challenge scope when needed.
Mentor mid-level engineers and participate in hiring and technical assessments.