Staff Data Engineer

Accountabilities

• • Design, build, and own near real-time data pipelines using CDC, streaming ingestion, and event-driven architectures to power the platform’s core data flows.
  • Evaluate, implement, and maintain vector database infrastructure and embedding pipelines supporting semantic search, retrieval-augmented generation, AI agents, and other AI-enabled applications.
  • Build scalable ELT and ETL pipelines that ingest data from internal platforms, financial systems, CRM, HRIS, and other sources for both real-time and batch use cases.
  • Partner with senior data leadership to architect the warehouse or lakehouse as the supporting system of record beneath streaming and AI infrastructure.
  • Develop lightweight transformation layers using technologies such as dbt to help Analytics Engineering teams turn raw data into reliable, business-ready datasets and metrics.
  • Own data pipeline reliability and observability, including monitoring, automated failure alerting, lineage tracking, and operational troubleshooting across streaming and batch environments.
  • Establish the technical foundation for self-service and AI-powered reporting while partnering with BI, Product, and Engineering teams on executive and departmental reporting needs.
  • Implement and maintain data governance practices covering documentation, access controls, lineage, and data quality standards.
  • Collaborate with Finance, Marketing, Customer Success, Operations, and other stakeholders to translate business requirements into reliable, low-latency data products.
  • Leverage AI-augmented development tools such as Claude Code or comparable solutions to accelerate engineering, testing, documentation, and workflow automation.
  • Contribute to the evolution of the broader data architecture and establish scalable engineering practices as the platform grows.

  Requirements

  • 7+ years of hands-on data engineering experience, including substantial depth in streaming and event-driven architectures rather than exclusively batch processing.
  • Proven experience designing and building production-grade near real-time pipelines from the ground up using technologies such as Kafka, Kinesis, Flink, Debezium, CDC, or similar tools.
  • Hands-on production experience with vector databases and embedding infrastructure, such as Pinecone, Weaviate, pgvector, Milvus, Zilliz, or comparable technologies.
  • Experience developing embedding strategies and chunking approaches for retrieval and AI-powered use cases is strongly valued.
  • Advanced proficiency in SQL and Python.
  • Working knowledge of cloud warehouse or lakehouse platforms such as Snowflake, BigQuery, or Databricks, as well as dbt.
  • Proven experience building or materially contributing to an end-to-end production data environment, ideally as an early, founding, or highly autonomous data engineering hire.
  • Familiarity with B2B SaaS data models, including customer lifecycle, sales pipeline, conversion, recurring revenue, ARR, CAC, and churn concepts.
  • Working knowledge of BI and reporting tools such as Looker, Tableau, Power BI, or similar platforms.
  • Familiarity with ELT/ETL tools such as Fivetran or Airbyte for batch data integration.
  • Strong understanding of data observability and reliability practices, including automated alerting, lineage tracking, monitoring, and failure recovery.
  • Experience using AI-augmented engineering and development tools such as Claude, Copilot, or similar technologies.
  • Ability to work independently, manage complex multi-stakeholder initiatives, and make sound technical decisions in a lean, fast-moving environment.
  • Comfortable operating with ambiguity and building new infrastructure, processes, and standards where limited existing foundations are in place.

  Benefits

  • Competitive compensation package based on skills, experience, qualifications, and work location.
  • Health benefits for eligible U.S.-based employees.
  • Flexible paid time off.
  • Parental leave.
  • Fertility and adoption assistance.
  • 401(k) benefits.
  • Educational reimbursement.
  • Opportunities to work with modern streaming, AI, vector database, and data platform technologies.
  • Meaningful ownership over the development of a new end-to-end data platform.
  • Support for reasonable accommodations throughout the application and interview process.
  • A collaborative environment focused on building scalable technology and continuously improving data capabilities.