Remote opportunity at
Clear StreetSenior Data Engineer
Clear Street is a financial services firm founded in 2018, focused on modernizing capital markets through a cloud-native clearing and custody infrastructure. The organization is seeking…
Career Tools
About This Role
Clear Street is a financial services firm founded in 2018, focused on modernizing capital markets through a cloud-native clearing and custody infrastructure. The organization is seeking a Senior Data Engineer to manage data accuracy across growth, user journey, and brokerage operations. This team member will take full ownership of information reliability as it moves in and out of the warehouse, building stable layers using dbt and establishing reliable contracts with…
Job Description
Clear Street is a financial services firm founded in 2018, focused on modernizing capital markets through a cloud-native clearing and custody infrastructure. The organization is seeking a Senior Data Engineer to manage data accuracy across growth, user journey, and brokerage operations.
This team member will take full ownership of information reliability as it moves in and out of the warehouse, building stable layers using dbt and establishing reliable contracts with product engineering. The position suits an independent engineer who prefers observable systems, tracks down dependencies without waiting for detailed specifications, and evaluates numbers with high skepticism.
The role operates within the Active Data group, managing data ingestion from advertising platforms, CRMs, support systems, and internal brokerage software. Core responsibilities include governing access permissions, setting up retention rules, managing PII masking, and ensuring every outbound sync accurately reflects source information. Professionals in this role collaborate directly with product, marketing, and operations groups to guarantee that business metrics remain precise and delivered on time.
Responsibilities
- Manage end-to-end data integrity for inbound feeds and outbound syncs
- Develop and maintain data warehouse models within dbt including raw landing, staged, identity-resolved, and curated layers
- Establish reliable data contracts with product engineering and external platform owners
- Implement governance protocols covering role-based access, data retention, PII masking, and auditability
- Select appropriate loading methods such as batch, streaming, or change-data-capture based on consumer needs
- Collaborate with Marketing, Product, and Operations departments to capture necessary business events and fields
Requirements
- Minimum of five years of professional experience in data engineering or analytics engineering with production pipeline accountability
- Advanced proficiency in Python and SQL
- Practical production background utilizing dbt for layered data warehouse development
- Hands-on work integrating application and third-party data via managed connectors, event pipelines, and change-data-capture tools
- Experience implementing reverse ETL processes, conversion feeds, or audience exports
- Background in monitoring data volume, freshness, and performing system-of-record reconciliation
- Familiarity with data governance practices including role-based access, retention policies, and audit trails
- Knowledge of brokerage or fintech domain concepts such as account creation, KYC, transfers, trading, and settlement
Qualifications
- Expertise in cross-platform identity resolution covering web, application, and user account records
- Familiarity with marketing data sources including ad-platform APIs, click IDs, attribution methods, and conversion APIs
- Experience configuring lifecycle and CRM messaging tool pipelines
- Background building semantic layers for business intelligence tools and AI agents
- Proficiency with workflow orchestration tools such as Airflow or Argo Workflows
- Working knowledge of Kubernetes for debugging personal execution jobs
- Understanding of strict audit and data retention mandates within regulated environments like FINRA and SEC
- Prior experience operating as an early data engineering team member during corporate scaling phases
Core Skills
Benefits
- Competitive financial compensation
- Company equity offerings
- 401(k) retirement matching program
- Gender-neutral parental leave
- Full medical, dental, and vision insurance coverage
- In-office perks including lunch stipends, stocked kitchens, and company happy hours
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
What is the compensation range for this role?
The base salary range spans from $150,000 to $250,000 per year, accompanied by additional benefits such as company equity, a 401(k) match, and insurance coverage.
What is the remote work policy?
The job posting specifies the employment type and salary, but does not provide details regarding a remote work policy.
What technical stack does the team use?
The Active Data stack utilizes Snowflake, dbt, SQL, Python, Fivetran, Segment, Argo Workflows, and Datadog.
What level of experience is required?
Candidates must have a minimum of five years of professional background in data engineering or analytics engineering, with direct accountability for production pipelines.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.