Senior Data Engineer

Role details

Job Description Clear Street is modernizing the brokerage ecosystem. Founded in 2018, Clear Street is a diversified financial services firm replacing the legacy infrastructure used across capital markets. We started from scratch by building a completely cloud-native clearing and custody system designed for today's complex, global market. Our platform is fully integrated with central clearing houses and exchanges to support billions in trading volume per day. We've agonized about our data model abstractions, created horizontal scalability, and crafted thoughtful APIs. All so we can provide a best-in-class experience for our clients. By combining highly-skilled product and engineering talent with seasoned finance professionals, we're building the essentials to compete in today's fast-paced markets. The Team: The mission of the Clear Street Active team is to provide best execution for every asset class in every market. Active is building a new, state-of-the-art, cloud-based trading platform giving high-performance traders access to liquidity venues across multiple asset classes, cutting-edge charting, and sophisticated order handling. The Active Data team owns how the business understands itself, and increasingly how it decides what to do next. Data flows into our warehouse from ad platforms, product analytics, CRM and support systems, and the brokerage's own systems. It flows back out to the platforms that message our customers, bid for new ones, and manage relationships with them. Every hop is a place where data can be duplicated, delayed, or quietly redefined, and an error that leaves the warehouse does not stay a reporting problem. It spends ad budget on the wrong people, sends a customer the wrong message, or misstates a regulated number. Moving data is table stakes. Keeping it true as it travels is the problem this role owns. It is a small team with a wide surface area, and we are scaling it as the platform grows toward tens of thousands of accounts. The Role: Three areas of the business depend on this role, and you will own the data for all of them: 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. Across all three, you will: 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. What We Value: Engineers measured on whether the numbers are right, not on what was shipped Very high agency. Much of the work here begins before the requirement is clear: the source has no documentation, the field you need was never captured, and the team that owns it is busy shipping. We need someone who chases a dependency into another team and ships something reliable without a spec. Skepticism about data, including your own pipelines. Check grain, joins, and completeness before you trust a number. A preference for boring, observable systems over clever ones Clear communication with non-engineers: able to explain to a marketer why yesterday's number moved, and to negotiate a schema change with a product team the same afternoon Pragmatism: ship the version that works now, and improve it deliberately rather than waiting for the ideal one 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