3 months ago
Remote, United StatesStaff+
Responsibilities
- Lead architectural decisions and design reviews for scalable, reliable, cost-effective data platform components.
- Own observability, alerting, runbooks, failover strategies, and reprocessing protocols for tier-1 data pipelines.
- Design and implement production-grade batch pipelines using Snowflake and dbt across product, marketplace, employer, and candidate data.
- Establish standards for schema design, dimensional modeling, data contracts, data quality, lineage, access control, and cataloging.
- Architect streaming infrastructure with KSQL and Apache Flink for real-time and near-real-time matching, recommendation, and product decisioning.
- Build feature stores, training data pipelines, and inference-ready datasets that support machine learning and AI systems.
- Improve analytics delivery through well-modeled, documented, and performant data products.
- Implement validation frameworks, SLA monitoring, anomaly detection, and privacy-by-default data systems.
- Mentor data engineers through code reviews and design guidance, and drive technical standards across teams.
- Identify and resolve complex data integration, performance, reliability, and architectural issues.
Requirements
- 10+ years of data engineering experience, including 3–5 years operating at a principal or staff individual-contributor level in a startup environment.
- Production-grade experience designing, owning, and scaling Snowflake and dbt.
- Experience with real-time streaming technologies such as KSQL or Apache Flink, preferably in production.
- Hands-on AWS experience at scale, including cloud data architecture, cost governance, and infrastructure-as-code; Terraform is a nice-to-have.
- DataOps experience with CI/CD for data pipelines, automated testing frameworks, and deployment practices.
- Significant demonstrated use of AI tools such as Claude, Copilot, Cursor, or similar in day-to-day engineering work.
- Expert-level Python and SQL proficiency, with strong distributed-systems, data-pipeline, and performance-optimization skills.
- Strong experience with dimensional modeling, schema design, data contracts, and data infrastructure supporting ML/AI systems.
- Track record of establishing technical standards through RFCs, architecture reviews, or platform migrations.
- Practical production experience with data governance, lineage, cataloging, access control, and quality monitoring.
- Familiarity with streaming, event-driven design, and change data capture architectures.
- Exceptional communication and stakeholder-management skills, with the ability to explain infrastructure trade-offs to product and business audiences.
Benefits
- Purpose-driven organization.
- Flexible time off.
- Remote-first work arrangement.
- Flexible work hours across multiple time zones.
- Medical and dental plans.
- Parental leave.
- Employee stock options.
Tech Stack
Categories
Data Engineering
