Senior Data Engineer (Foundations)

Stellantis•Auburn Hills, MI

10h

About The Position

The Senior Data Engineer – Platform Foundation is a hands-on, senior-level contributor 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 making the platform reliable, extensible, and easy for other teams to adopt. The Foundations squad works across simplifying the data platform landscape, enabling structured and unstructured data ingestion at scale, and supporting the exposure of data products to internal consumers. The engineer contributes across these areas by making architectural decisions, writing production code, and enabling other teams through documentation and hands-on support. The role develops reusable ingestion components with dlt and dbt-core; owns the Airflow platform, including DAGs, shared operators, deployments, and upgrades; and makes incremental loading, data quality, and lineage metadata integral to pipelines. It also drives standardization across teams, collaborates with Solution Architects on platform architecture, supports governed data-product interfaces, and contributes to Terraform-managed, multi-cloud infrastructure across AWS and Azure. The engineer uses and evaluates AI-assisted development tools, promotes responsible adoption, and explores AI-powered capabilities for documentation and self-service. Additional responsibilities include maintaining CI/CD pipelines, setting observability standards, participating in on-call and incident improvement work, producing enablement materials, translating changing business requirements into technical designs, mentoring mid-level engineers, and participating 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 Python skills, including writing idiomatic and testable code.
Advanced dbt-core skills, including modelling with custom materializations, testing, documentation, and packages.
Apache Airflow skills, including DAG design patterns, custom operators, dynamic task mapping, and SLA management.
Experience 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 data.
Own the Airflow platform end-to-end by extending and maintaining DAGs and shared operators, handling deployments and version upgrades, and supporting 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 and drive standardization onto shared Platform Foundation components.
Collaborate with Solution Architects to evolve platform architecture in response to new data sources and changing business requirements.
Support data product exposure by defining and implementing governed interfaces for reliable access by internal consumers.
Contribute to Terraform-managed infrastructure and participate in AWS and Azure multi-cloud deployment patterns.
Use and evaluate AI-assisted development tools to accelerate platform delivery; champion adoption and share best practices and guardrails for reviewing AI-generated code.
Explore AI-powered capabilities, including RAG pipelines and LLM-assisted data cataloguing, for internal platform 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 concrete technical designs and challenge scope when needed.
Mentor mid-level engineers and participate in hiring and technical assessments.