Data Engineer

• We are looking for a hands-on Data Engineer to help build and scale our modern data platform. In this role, you will work closely with Finance, Engineering, Product, and Analytics teams to develop reliable, high-performance data pipelines and systems
• You’ll contribute to both batch and real-time data processing using technologies like Databricks, AWS, kafka and several 3rd party data, while helping ensure data quality, accessibility, and usability across the organization
• You’ll play a key role in enabling data activation, ensuring that high-quality data flows not only into the warehouse but also outward to business tools such as Salesforce etc
• Additionally, you will help power next-generation AI-driven applications, including agent-based systems and AI-driven tools using OSS tech, by building robust data foundations and pipelines
• Build and maintain scalable data pipelines (batch and streaming) using tools such as Databricks, Spark, Kafka, and AWS services
• Build and maintain pipelines from source systems (Salesforce, billing, product events, API logs) into clean analytics layers
• Design, develop, and optimize ETL/ELT workflows using DBT, PySpark, SQL, and tools like Fivetran
• Work closely with finance in developing Finance data solutions, Finance metrics and forecasting models
• Partner with Finance on revenue accounting, COGS, and margin reporting
• Partner closely with marketing and growth teams to enable data use cases such as segmentation, campaign targeting, and lifecycle analytics
• Develop and maintain reverse ETL pipelines to sync data from the warehouse to tools like Salesforce, HubSpot, Braze, and other downstream systems
• Create and manage curated datasets to support analytics, reporting, and go-to-market initiatives
• Build and maintain dashboards and reporting layers to support marketing and business performance tracking
• Support AI/ML and agent-based applications by preparing and serving high-quality datasets for MCP (Model Context Protocol) integrations and AI-driven applications
• Monitor pipeline performance, troubleshoot issues, and ensure high data reliability and quality
• Implement data quality checks, validations, and alerting mechanisms across both ingestion and activation layers
• Collaborate with cross-functional teams to define data contracts and ensure consistency across systems

Benefits

• A remote-first work environment with hubs in California, NYC, and Canada that offer monthly in-person gatherings
• Unlimited PTO with 11 U.S. holidays observed and a week shutdown in December to rest and recharge
• Competitive health insurance plan, where 100% of the policyholder is covered and 75% for dependents
• 401k program, 3% match coming January 2025
• Chance to collaborate with a team at the forefront of AI research
• $1,200 / year Health & Wellness Allowance to support your personal goals
• 12 weeks of paid paternity leave in the US; additional time off is also considered
• $500 work-from-home stipend to be used up to a year of your start date
• In-person coworking weeks 1 to 2 times a year- Experience creating dashboards and supporting reporting workflows (BI tools) for both internal and external audiences
• Understanding of data modeling and working with large-scale datasets (batch and streaming)
• Strong hands-on experience with Databricks, AWS (S3, Glue, Athena, EMR, etc.), and Kafka
• 6+ years of experience in data engineering or a related field
• Familiarity with reverse ETL / data activation workflows and syncing data to tools like Salesforce, HubSpot, Braze
• Experience building and maintaining ETL/ELT pipelines (DBT/Airflow or similar experience preferred)
• Strong communication skills and ability to work collaboratively across teams
• Proficiency in Python (PySpark) and SQL for large-scale data processing
• Strong problem-solving skills and ability to debug production data issues
• Experience with data ingestion tools such as Fivetran (or similar)
• Experience working with Finance and building finance specific metrics and pipelines
• Familiarity with agent-based systems, MCP integrations, or LLM-powered applications
• Exposure to or experience with AI/ML data pipelines, including RAG architectures, vector databases, or embeddings workflows