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.

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