Principal Data Engineer

Role overview Set the technical direction and engineering standards for an enterprise data platform. This hands-on technical leadership role spans the full data lifecycle, shaping systems that make reliable, secure, and timely data useful to analytics, applications, and business teams.

Responsibilities - Design and guide scalable data platforms and pipelines across batch and streaming ingestion, transformation, storage, orchestration, serving, and consumption. - Document architecture choices and tradeoffs involving performance, reliability, scale, latency, security, maintainability, and cost. - Establish reusable patterns for data integration, event flows, schema changes, data quality, and domain-oriented models. - Set practices for testing, deployment, monitoring, incident response, infrastructure, access controls, governance, and data lifecycle management. - Lead technical reviews and cross-team decisions, assess platform options, and address architectural risks and technical debt. - Mentor engineers, run design and knowledge-sharing sessions, and develop templates and playbooks that help teams deliver consistent data products.

Requirements - 10 or more years in software, data, platform engineering, or a related technical field, including 5 or more years designing and operating large-scale data systems or products. - Strong end-to-end system design experience, from requirements and tradeoffs through production delivery. - Knowledge of modern data architecture, including warehouse or lakehouse platforms, batch and streaming, modeling, orchestration, metadata, and governance. - Proficiency in SQL and a general-purpose language such as Python, Java, or Scala. - Experience with distributed data technologies and platforms such as Spark, Kafka, Flink, Snowflake, Databricks, BigQuery, or Redshift. - Ability to establish engineering standards and influence technical decisions across teams; strong written and verbal communication.

Nice to have - Familiarity with tools and platforms such as Airflow, dbt, cloud data warehouses, data integration frameworks, and monitoring or data-quality systems.

Benefits and work setup - The listing gives a US base salary range of $171,500–$269,500. Additional compensation may apply; the offer depends on location, qualifications, skills, and experience. - Benefits and wellbeing programs are mentioned, but specific plan details are not provided.