Clear Street
Clear Street
Clear Street izstrādā finanšu infrastruktūru institucionālajiem klientiem, vienā platformā apvienojot norēķinu, aktīvu glabāšanas, darījumu izpildes un galvenā brokera pakalpojumu iespējas. Uzņēmuma mākoņdatošanas tehnoloģija ir veidota, lai aizstātu novecojušas finanšu sistēmas un nodrošinātu vienotu darbības pamatu dažādām aktīvu klasēm, valstīm un valūtām. Uzņēmums apvieno finanšu, tehnoloģiju un saistīto operatīvo jomu komandas, lai modernizētu veidu, kā institūcijas pārvalda sarežģītus globālos tirgus.

Senior Data Engineer, Remote (United States)

Lead reliable data pipelines, dbt models, and governance for Clear Street’s cloud-based brokerage and trading platform. Keep marketing, customer lifecycle, and regulated brokerage data accurate from collection through use.

Apraksts

  • Build and manage data systems for growth, customer lifecycle journeys, and brokerage operations.
  • Ingest spend, clicks, conversions, and related data from paid, affiliate, organic, application, CRM, support, and brokerage systems.
  • Reconcile marketing and brokerage data with authoritative systems of record.
  • Send funded-account outcomes and other conversion data to advertising platforms.
  • Connect customer identity across the journey from anonymous visit through sign-up, KYC, funding, and first trade.
  • Keep lifecycle messaging, CRM, and support systems synchronized with actual customer events.
  • Model account opening, KYC, funding, transfers, trading, and settlement data to regulated broker-dealer standards.
  • Ensure inbound feeds are complete and timely, and outbound data syncs are delivered correctly.
  • Manage dbt warehouse layers, including raw, staged, identity-resolved, and curated models.
  • Define data contracts with product engineering and external platform owners.
  • Manage governance for role-based access, PII masking, retention, auditability, and self-service permissions.
  • Select batch, change-data-capture, or streaming approaches to fit consumer needs.
  • Work with Marketing, Product, and Operations to define required events and fields.
  • Collaborate with engineering and infrastructure teams responsible for trading systems, the core clearing ledger, cloud, Kubernetes, and networking.

Prasības

  • At least five years in data engineering or analytics engineering, including responsibility for production pipelines when they fail.
  • Strong working knowledge of SQL and Python.
  • Production dbt experience with layered raw, staged, and curated warehouse models, defined grains, tests, and incremental builds.
  • Hands-on experience ingesting third-party and application data through managed connectors, event pipelines, and change-data-capture.
  • Experience addressing schema drift, resynchronizations, duplicates, and late or missing records.
  • Experience delivering data through reverse ETL, audiences, or conversion feeds, and ensuring its accuracy after export.
  • Experience reconciling against systems of record, monitoring freshness and volume, and preventing recurring incidents.
  • Experience with role-based access, PII masking, retention, and auditable data governance.
  • Familiarity with fintech or brokerage data, including account opening and KYC, funding and transfers, trades, and settlement.
  • Self-directed approach and ability to work effectively without a large escalation team.

Priekšrocības

  • Competitive pay, benefits, and perks.
  • Company equity.
  • 401(k) matching.
  • Gender-neutral parental leave.
  • Comprehensive medical, dental, and vision insurance.
  • Lunch stipends.
  • Fully stocked kitchens.
  • Team happy hours.
  • Chance to join a small, growing team.
  • A modern, high-quality codebase with little technical debt and scope to build services and features.
  • Merit-based culture that values collaboration.

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