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Salary: USD 150,000 - 250,000 / annual
Clear Street is a diversified financial services firm modernizing the brokerage ecosystem with a cloud-native clearing and custody system. The Active Data team owns how the business understands itself and increasingly how it makes decisions. This role is responsible for data integrity, warehouse architecture, and governance across three critical business areas: Growth (paid/affiliate/organic channel attribution and ad platform optimization), Lifecycle and user journey (identity resolution from first visit through KYC, funding, and first trade), and Brokerage data (account opening, KYC, funding, transfers, and trading with regulatory precision).
You will own data integrity in both directions—proving every feed arrives complete and on time, and that every outbound sync lands correctly. You'll own warehouse layers in dbt (raw landing, staged identity-resolved models, and curated models for analysts, data scientists, dashboards, and AI agents), with every model having a declared grain and tests. You will establish data contracts with product engineering and upstream platform owners to catch changes at the boundary rather than silently breaking downstream models. You'll own governance including role-based access, PII masking, retention, and auditability with self-serve tools that don't circumvent permissions. You'll choose deliberately between batch, CDC, and streaming based on consumer needs, and partner with Marketing, Product, and Operations to ensure the business captures the events and fields it needs.
The role is measured on whether the business's numbers are right and on time, not on pipelines delivered. You'll work with a small, growing team where much of the work begins before requirements are clear—chasing dependencies across teams and shipping reliable solutions without a spec. The team values skepticism about data (including your own pipelines), boring observable systems over clever ones, clear communication with non-engineers, and pragmatism.
You will not own the trading systems, core clearing ledger, or company infrastructure (cloud, Kubernetes, networking)—those are owned by engineering and infrastructure teams—but you'll work closely with them.
Tech stack: Snowflake, dbt, SQL, Python, Fivetran, Segment, Argo Workflows, Datadog.
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 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; handling 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 fixes that stop incidents from recurring
- Data governance in practice: role-based access, PII masking, retention, and audit trails suitable for regulators
- Familiarity with fintech or brokerage data: account opening and KYC, funding and transfers, trades and settlement, and understanding why superficially similar balance or status fields are not interchangeable
- Self-starter who operates well without a large team to escalate into
NICE TO HAVES:
- Identity resolution across web, app, and account data (anonymous visitors, multiple devices, multiple accounts per person)
- Marketing data (ad-platform APIs, conversion APIs, click IDs, attribution)
- Lifecycle and CRM tooling where pipelines end in campaigns rather than tables
- 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 regulated environments (SEC, FINRA)
- Experience as an early data engineering hire on a team that scaled
