Data Engineer

Responsibilities

• Write production-quality code for data ingestion, transformation, orchestration, and monitoring.
• Design, build, and maintain reliable, scalable data pipelines and data platforms, including batch or distributed processing workloads (e.g., Spark-based pipelines).
• Partner with actuaries, analytics, data science, and business teams to enable modeling and AI uses.
• Apply AI-assisted engineering approaches, including LLM-enabled tools or agents, to improve data quality, observability, documentation, and productivity.
• Identify data quality issues, bottlenecks, and failure modes; design systems that are resilient and observable.
• Stay current with data engineering and AI platform advancements, evaluate new tools, and recommend adoption where appropriate.
• Apply professional skepticism and alternate approaches to validate data correctness, lineage, and assumptions.
• Communicate system design, trade-offs, and limitations clearly to technical and non-technical stakeholders.
• Provide support and guidance to others who are at earlier stages in their data engineering or AI journey.

Qualifications

• 4–7 years of relevant data engineering, software engineering, or technical experience.
• A Master’s degree in Data Engineering or Computer Science.
• Familiarity with cloud data platforms and distributed processing frameworks (e.g., Databricks, Snowflake, Spark, or similar), and modern data engineering tooling.
• Strong programming skills, particularly in Python and SQL (including experience with distributed or batch processing frameworks such as PySpark or equivalent), with an emphasis on maintainable, testable code.
• Experience designing and operating data pipelines, data lakes/warehouses, or distributed data systems.
• Experience applying AI, machine learning, or LLM-based tools to real engineering problems (e.g., building agents, calling model APIs, integrating AI into engineering workflows).
• Experience working with large or complex data flows and creating defensible system designs and implementation plans.
• Strong professional judgment, curiosity, and attention to detail.