Staff Data Engineer

• →Design, build, and own near real-time data pipelines (CDC, streaming ingestion, event-driven architectures) as the backbone of the platform's data flow
• →Evaluate, implement, and maintain vector database infrastructure and embedding pipelines to support AI-augmented use cases (semantic search, retrieval-augmented generation, AI agents acting on company data).
• →Design and build ELT/ETL pipelines ingesting data from our platform, financial platforms, CRM (HubSpot) and HRIS, feeding both real-time and batch use cases.
• →Partner with the Sr. Director to architect the underlying warehouse/lakehouse as a supporting system of record — the storage layer beneath the streaming and AI infrastructure.
• →Build lightweight transformation layers (e.g., dbt) as needed to enable our Analytics Engineering team translate raw data into business-ready datasets aligned to core metrics like ARR, CAC, and churn.
• →Own pipeline reliability and observability — monitoring, automated failure alerting, and lineage tracking across both streaming and batch pipelines.
• →Build the technical foundation for self-service and AI-powered reporting, partnering with BI, Product & Engineering stakeholders on recurring executive and departmental reports.
• →Implement data governance practices, including documentation standards and access controls.
• →Partner cross-functionally with Finance, Marketing, Customer Success, and Operations to translate data needs into reliable, low-latency data products.
• →Leverage AI-augmented development workflows (e.g., Claude Code) to accelerate pipeline development and documentation.

• 7+ years of hands-on data engineering experience, with meaningful depth in streaming/event-driven systems, not just batch pipelines.
• Proven experience designing and building near real-time pipelines from scratch (e.g., Kafka, Kinesis, Flink, Debezium/CDC) in a production environment.
• Hands-on production experience with vector databases and embeddings (e.g., Zilliz, Pinecone, Weaviate, pgvector, Milvus) — ideally having built this infrastructure from the ground up rather than just consuming a managed AI feature.
• Advanced proficiency in SQL and Python.
• Working knowledge of a cloud warehouse/lakehouse platform (Snowflake, BigQuery, or Databricks) and dbt — you'll use these, but they're the storage/transform layer supporting the streaming and AI work, not the main focus.
• Proven experience building or materially contributing to an end-to-end production data environment, ideally as an early or founding data hire.
• Familiarity with B2B SaaS and recurring revenue data models (customer lifecycle, pipeline/conversion data).
• Working knowledge of BI/reporting tools (e.g., Looker, Tableau, Power BI) as a downstream consumer of your data models.
• Ability to work independently and drive multi-stakeholder projects in a lean, scrappy, fast-moving environment — comfortable with ambiguity and building without a lot of existing infrastructure or process.

Harbor Compliance is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law.

• Strong command of event streaming and CDC tooling.
• Hands-on experience with vector databases (Pinecone, Weaviate, pgvector, Milvus, or similar), including embedding strategies and chunking approaches for retrieval use cases.
• Working knowledge of ELT/ETL tools (e.g., Fivetran, Airbyte) for batch use cases.
• Working knowledge of data observability/reliability practices — automated alerting, lineage tracking.
• Fluency with AI-augmented development tools (e.g., Claude, Copilot) to accelerate engineering and documentation.

Harbor Compliance is committed to providing any reasonable accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

Harbor Compliance uses third-party automated tools and skills assessments (such as Testlify and Rippling) to help evaluate job applications and streamline our hiring workflow. These tools assist our recruitment team in reviewing qualification trends and scheduling, but all final employment decisions are made solely by humans on our Talent Success team, and all tools are subject to human oversight.

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