Senior Data Engineer

Clear Street is a financial services firm modernizing brokerage infrastructure. The Senior Data Engineer owns data pipelines into and out of the warehouse, data models, data integrity, and governance for growth, customer lifecycle, and brokerage data.

Responsibilities:

• Growth. Bring in spend, clicks, and conversions from every paid, affiliate, and organic channel, reconcile them against our own records, and send funded-account outcomes back to the ad platforms, so bidding optimizes for customers who actually fund and trade
• Lifecycle and the user journey. Resolve each customer's identity from a first anonymous visit through sign-up, KYC, funding, and first trade, and keep lifecycle messaging, CRM, and support systems in step with what actually happened
• Brokerage data. Model account opening, KYC, funding and transfers, and trading from the platform's own systems, with the precision a regulated broker-dealer needs
• Own data integrity in both directions. Prove that every feed arrived complete and on time, and that every outbound sync landed what it should. A duplicated conversion inflates ad spend, a stale attribute sends the wrong message, and a broken join misstates funded accounts to leadership
• Own the warehouse layers in dbt: raw landing, staged and identity-resolved models, and the curated models that analysts, data scientists, dashboards, and internal AI agents read from. Every model has a declared grain and tests
• Establish data contracts with product engineering and with the owners of the platforms we depend on, so an upstream change is caught at the boundary instead of silently nulling a field three models downstream. You will define these rather than inherit them
• Own governance: role-based access, PII masking, retention, and auditability, including self-serve tools that do not become a way around permissions
• Choose deliberately between batch, change-data-capture, and streaming, based on what each consumer actually needs
• Partner with Marketing, Product, and Operations to get the events and fields the business needs captured. This role is measured on whether the business's numbers are right and on time, not on pipelines delivered

Requirements:

• Minimum five years in data engineering or analytics engineering, with production pipelines you were on the hook for when they broke
• Deep proficiency in SQL and Python, and production experience with dbt in a layered warehouse: raw, staged, and curated models with a declared grain, tests, and incremental builds
• Hands-on experience bringing in third-party and application data through managed connectors, event pipelines, and change-data-capture, and handling what goes wrong with each: schema drift, resyncs, duplicates, and late or missing records
• Experience sending data back out, through reverse ETL, audiences, or conversion feeds, and owning its correctness after it leaves the warehouse
• Data quality in practice: reconciliation against systems of record, freshness and volume monitoring, and the fix that stops an incident from recurring
• Data governance in practice: role-based access, PII masking, retention, and an audit trail you could show a regulator
• Familiarity with fintech or brokerage data: account opening and KYC, funding and transfers, trades and settlement, and why superficially similar balance or status fields are not interchangeable
• Self-starter who operates well without a large team to escalate into
• Identity resolution across web, app, and account data: anonymous visitors, multiple devices, and more than one account per person
• Marketing data: ad-platform APIs, conversion APIs, click IDs, and attribution
• Lifecycle and CRM tooling, where the pipeline ends in a campaign rather than a table
• Semantic layers for BI tools and AI agents
• Workflow orchestration (Argo Workflows, Airflow, or similar), and enough Kubernetes to debug your own jobs
• Data retention and audit requirements in a regulated environment (SEC, FINRA)
• Experience being an early data engineering hire on a team that scaled