Sr Data Platform Engineer / Platform Lead
Position Summary
As a Data Platform Engineer/Platform Lead within Digital Products, you'll help build and run the data platform behind our fleet and telematics products from ingesting raw data through to the datasets and views that power our customer-facing applications. You'll work closely with Data Science and product teams, balancing hands-on pipeline development with platform-level decisions on cost, reliability, and scale.
Responsibilities
Design, build, and maintain data pipelines and orchestration workflows that move data from ingestion through transformation to serving layers
Write and optimize SQL and PySpark for large-scale data processing and transformation
Tune cloud data warehouse performance and cost (query optimization, refresh/scheduling strategy, resource sizing) as data volume and usage scale
Build and maintain data quality checks and pipeline monitoring/alerting to catch issues before they reach downstream consumers
Partner with Data Science to support feature engineering and analytics pipelines feeding ML/analytics workloads
Collaborate with product and engineering stakeholders to translate business needs into reliable, scalable pipeline design
Support incident response, root-cause analysis, and continuous improvement of the data platform
Document architecture and pipeline design to support onboarding of new team members
Contribute to platform-level decisions on architecture, tooling, and technical direction as the team and data volume scale
Core Skill Set Required
Strong SQL skills, including query optimization
Proficient in Python and PySpark for large-scale/distributed data processing
Hands-on experience with a workflow orchestration tool (e.g., Apache Airflow)
Experience with a cloud data warehouse (e.g., Snowflake)
Experience with relational databases (e.g., PostgreSQL)
Experience with cloud data infrastructure (AWS), including cloud storage and distributed processing (e.g., S3, EMR/Spark)
8+ years in data engineering with hands-on ownership of production data pipelines
Strong communication skills; able to explain technical tradeoffs to both technical and non-technical stakeholders
Good to Have
Familiarity with streaming or change-data-capture (CDC) ingestion patterns (e.g., Apache Kafka)
Experience building data quality frameworks and pipeline observability/alerting
Exposure to ML/analytics pipeline support (feature engineering, model-serving data prep)
Familiarity with monitoring and logging tools, such as Dynatrace, Prometheus, Grafana, or the ELK stack
Strong knowledge of DevOps principles, continuous integration, continuous delivery, and infrastructure automation
Hands-on experience with containerization technologies like Docker and container orchestration platforms like Kubernetes or OpenShift
Experience with CI/CD and collaboration tooling such as Bamboo, Bitbucket, and Confluence
Relevant certifications (e.g., SnowPro Core, SnowPro Advanced: Data Engineer, AWS Data Analytics)
Experience building or supporting SaaS products
Specialized experience with secure data sharing in Snowflake
