AI Data Engineer

Who you are

• Experience: 5+ years of professional experience in data engineering, analytics engineering, or a closely related role, ideally at a B2B SaaS or fintech company
• SQL and Python: Expert-level SQL and strong Python skills for building pipelines, transformations, and tooling; comfortable writing tested, production-grade code
• Modern data stack: Hands-on production experience with a cloud data warehouse (BigQuery strongly preferred), dbt or equivalent transformation framework, managed ingestion tools (Fivetran or similar), and an orchestrator (Airflow, Dagster, Cloud Composer, or similar)
• Data modeling: Deep understanding of dimensional modeling, layered warehouse architecture, and schema design, with strong opinions on grain, naming, and consistency
• Data quality and observability: Experience implementing testing frameworks, lineage, monitoring, and alerting for data pipelines, and operating them in production including on-call
• Engineering fundamentals: Fluency with git-based workflows, code review, CI/CD, and infrastructure-as-code; you treat data infrastructure as software
• Ownership: A track record of taking ambiguous, high-impact problems and delivering reliable systems end-to-end, with a focus on outcomes rather than just implementation
• Communication: Ability to explain technical trade-offs to non-technical stakeholders and drive alignment on data definitions across teams
• Experience with streaming or event data (Pub/Sub, Kafka, or similar) and product analytics tooling (Amplitude or similar)
• Experience with Terraform and Google Cloud Platform infrastructure
• Experience with observability platforms such as Datadog
• Exposure to financial, accounting, tax, or payroll data and the correctness requirements that come with it
• Experience building semantic layers or metric stores consumed by LLM-based tools, or supporting LLM evaluation programs
• AI-assisted development experience (Claude Code or similar)

What the job involves

• We are looking for a Data Engineer to own and scale the data platform that powers analytics, reporting, and AI across Collective
• You will design, build, and maintain the pipelines that move data from our product, financial, and third-party systems into our BigQuery warehouse; model that data into clean, well-documented, reliable tables; and set the engineering standards that keep the platform trustworthy as the company grows
• You will join the Data Engineering team within Engineering and work closely with product engineers, analysts, and business stakeholders across Operations, Finance, and Go-to-Market
• This is a hands-on role for someone who cares about data quality, takes ownership of production systems end-to-end, and wants their work to be the foundation the rest of the company builds on
• Design and build data pipelines. Develop, deploy, and maintain scalable batch and event-driven pipelines that ingest data from application databases, SaaS tools, and external APIs into BigQuery using managed connectors (Fivetran), custom Python loaders, and orchestration tooling
• Model the data. Design and implement dimensional and analytical data models in dbt, following a layered architecture (raw, staging, marts) with clear grain, naming conventions, and documentation that analysts and downstream tools can rely on
• Own data quality and reliability. Implement testing, monitoring, alerting, and data contracts across the pipeline; define and meet freshness and accuracy SLAs; triage and resolve pipeline failures and data incidents to root cause
• Optimize performance and cost. Tune warehouse queries, partitioning, and clustering; manage BigQuery spend; and keep pipelines efficient as data volume grows
• Establish engineering standards. Drive best practices for version control, code review, CI/CD, and infrastructure-as-code across the data stack; document systems and runbooks so the platform is maintainable by the team
• Govern and secure data. Implement access controls, PII handling, and data retention practices appropriate for a financial services company; partner with Security and Legal on compliance requirements
• Enable the business. Partner with product engineers on source schema design and change management, and with analysts and stakeholders to translate business questions into reliable datasets, metric definitions, and self-serve reporting in Metabase
• Support AI and analytics use cases. Maintain the semantic layer, metric definitions, and documentation that allow LLM-based tools and internal agents to query the warehouse accurately and consistently

Benefits

• Flex work setup
• Flexible PTO
• 14 paid holidays
• Paid parental leave (16 weeks)
• Health, dental, and vision
• Additional medical coverage
• 401k plan
• Equity