Designs, builds, and operates data tools, services, workflows, etc that deliver high value through the solution to key business problems by leveraging modern data engineering tools (e.g. Spark, Kafka, Storm, …) and orchestration tools (e.g. Google Workflow, AirFlow Composer)
Confidently optimizes design and execution of complex solutions in data ingestion and data transformation
Enables data products optimized for AI/ML and GenAI workloads—high throughput, observable, feature-ready and governed
Produces well-engineered software, including appropriate automated test suites, technical documentation, and operational strategy
Implements modular, reusable components and microservices that accelerate development and reduce operational overhead
Provides input into the roadmaps of upstream teams (e.g. Data Platforms, DataOps, DevOps) to help improve the overall program of work
Ensure consistent application of platform abstractions to ensure quality and consistency with respect to logging and lineage
Fully versed in coding best practices and ways of working, and participates in code reviews and partnering to improve the team’s standards
Adhere to QMS framework and CI/CD best practices and helps to guide improvements to them that improve ways of working
Provides technical leadership, code reviews, architectural guidance, and mentorship to junior engineers and serves as an escalation point for complex operational issues across pipeline and data services.

PhD + 2 years, Masters + 4 years or a Bachelors degree with 6+ years of Data engineering experience in industry
Software engineering experience
Experience overcoming high volume, high compute challenges
Familiarity with orchestrating tooling
Cloud experience
Experience in automated testing and design
Experience with DevOps-forward ways of working

Senior Data Engineer

Key skills