Senior Data Engineer
About the role
- (Must Have)
- Expert Python & SQL Developer
- Strong AWS Experience (Lambda, Glue, State Machines, Fargate, Cloud Formation, RedShift)
- Strong Experience with Semi-Structured Data (XML, CSV, JSON, Parquet)
- Strong Automation Experience
- Experience with Databricks (Delta Lake, Notebooks, SQL Analytics)
- Experience with Kubernetes (Cluster Orchestration, Container Networking, Pod Management)
- Experience with DevOps, CI/CD, Deployment Orchestration (Octopus Deploy)
- Experience with Agentic AI (LangGraph, MCP Servers, Agent Frameworks)
- Experience with Data Handling (Pandas / NumPy, Apache Spark, Ray)
- Helpful Experience (Nice to Have)
- Experience with LLMs, Machine Learning & Deep Learning Libraries, AI Application Frameworks, Vector Databases
- Experience with Automotive Data / Industry
- Experience with Agile / Scrum
- Analyze large, and complex, data sets
- Design, develop and coordinate the generation of Data Pipelines
- Work collaboratively with team to plan and solve complex problems
- Design for, or optimize existing pipelines, for performance and cost
- MOTORWork on high-impact data products used across the automotive industry
- Real ownership and influence over platform direction
- Backed by Hearst—stable, but with room to innovate
- Collaborative team solving meaningful, complex problems
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
