2 months ago
Remote, United StatesStaff+
Base Salary
$160k - $190k/yr
Responsibilities
- Lead architectural decisions and design reviews for scalable, reliable, and cost-effective data platform components.
- Design and implement production-grade batch pipelines using Snowflake and dbt across product, marketplace, employer, and candidate data.
- Establish data modeling standards covering schema design, dimensional modeling, and data contracts.
- Architect and evolve real-time and near-real-time streaming infrastructure using KSQL and Apache Flink.
- Build feature stores, training data pipelines, and inference-ready datasets that support AI and machine learning systems.
- Define and enforce data governance standards for quality, lineage, access control, cataloging, privacy, and compliance.
- Own observability, alerting, runbooks, failover strategies, reprocessing protocols, SLA monitoring, and anomaly detection for tier-1 pipelines.
- Mentor data engineers, conduct code reviews, provide design guidance, and raise technical standards across teams.
- Partner with engineering, product, analytics, and platform stakeholders to deliver reliable and performant data products.
- Identify and resolve complex data integration, architectural, performance, and reliability problems hands-on.
Requirements
- 10+ years of data engineering experience, including 3–5 years at a principal or staff individual-contributor level in a startup environment.
- Required production-grade experience designing, owning, and scaling Snowflake and dbt.
- Experience with AWS at scale, including cloud data architecture, cost governance, and infrastructure-as-code.
- Expert-level proficiency in Python and SQL, with strong knowledge of distributed systems, data pipeline design, and performance optimization.
- Strong experience with data modeling, including dimensional modeling, schema design, and data contracts.
- Experience building data infrastructure supporting ML and AI systems, including feature pipelines, training data management, and inference-layer data serving.
- Strong data governance experience covering lineage, cataloging, access control, quality monitoring, and production implementation.
- Familiarity with real-time and near-real-time architectures, including streaming, event-driven design, and CDC.
- Demonstrated experience setting technical standards across teams through RFCs, architecture reviews, or platform migrations.
- Significant day-to-day use of AI tools such as Claude, Copilot, Cursor, or similar for architecture, coding, review, and problem solving.
- Strong communication and stakeholder management skills, including the ability to explain infrastructure trade-offs to product and business audiences.
- Experience with KSQL or Apache Flink and production streaming pipelines is strongly preferred.
- Terraform experience is a nice-to-have.
- Familiarity with DataOps practices, automated testing frameworks, and deployment practices for data infrastructure is expected.
Benefits
- Purpose-driven organization.
- Flexible time off.
- Remote-first work arrangement.
- Flexible work hours for employees across multiple time zones.
- Medical and dental plans.
- Parental leave.
- Employee stock options.
Tech Stack
Categories
Data Engineering
