Senior Data Engineer
External
Raylu-ai · New York City$165K–$230K/yrFull-timeOn-site4mo ago
ComplianceDocumentationETLPython
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Responsibilities
- Own end-to-end data flows: ingestion, normalization, entity resolution, enrichment, and delivery.
- Stand up monitoring for freshness, completeness, and accuracy; drive RCA and prevention.
- Build internal tools that make data discoverable and usable by engineering and product.
- Recruit, onboard, and manage BPO vendors; negotiate and run data partnerships.
Requirements
- Core stack : Python, Dagster, DuckDB
- Pipelines at scale : Building resilient ELT/ETL with strong contracts, idempotency, and lineage.
- Data operations : Set quality bars, manage BPO workflows, and run SLAs with external data partners.
- Serving & access : Position data for production use from serving infrastructure, documentation, and SLAs for internal consumers.
- Cost & performance : You tune storage/compute and keep a sharp eye on unit economics.
- Opinionated: Deep level of understanding of the technological landscape, making both high level system and granular code design decisions based on understanding rather than preference - diving deep on unknown patterns in order to build the best product.
- Experience with big data, columnar storage formats, vector indexes, and privacy/compliance in data products.
- Logistics :
- Compensation: $165K - $230K salary, competitive equity (4 year vest)
- Location: New York City. We are a fully in-office team working out of Midtown Manhattan Monday through Friday. We allow for WFH days when anyone is traveling, but we do not allow for permanent remote work.
Benefits
Generous health, dental, and vision insurance401k with 3% automatic contribution (no vesting)Paid LunchesWellness and Citi Bike benefitUnlimited PTOHealth insuranceDental insuranceVision insurance401(k)Paid time offRemote work optionsEquity / stock options
Additional Information
Position Summary We're hiring a Senior Data Engineer to own data at truly massive scale. You'll design and run pipelines that clean, enrich, and serve data spanning hundreds of attributes across 80M+ companies and 800M+ people. The role blends classic data engineering with data operations, vendor/BPO orchestration, and data partnerships.
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