Lead Data Engineer

What You'll Do

• Design and build Eve’s data warehouse architecture from the ground up, including schema design, data modeling, and pipeline orchestration.
• Develop and maintain reliable ETL/ELT pipelines that integrate data across core business systems (CRM, product, billing, marketing, etc.).
• Define and implement core business data models that standardize how Eve measures pipeline, conversion, revenue, customer health, and attribution.
• Build a reliable GTM data layer that enables marketing, sales, CS, and finance teams to self-serve key insights without ad hoc analysis.
• Establish data quality, governance, and monitoring practices to ensure accuracy and reliability across the data stack.
• Create cross-functional intake and prioritization processes for data requests, balancing infrastructure work with business needs.
• Partner closely with GTM, finance, product, and engineering teams to understand their workflows and translate them into durable data systems.
• Select and manage Eve’s data stack (warehouse, orchestration, transformation, BI tooling).
• Lay the foundation for product analytics and behavioral event tracking across Eve’s platform.
• Build and scale Eve’s data function over time, including hiring, team structure, and operating processes.

What You'll Bring

• Proven experience building a data warehouse and core data infrastructure from scratch at a high-growth SaaS or technology company.
• Deep expertise in data engineering, including data modeling, ETL/ELT pipeline design, and warehouse architecture.
• Strong proficiency with SQL and modern data stack tools (e.g., Snowflake/BigQuery/Redshift, dbt, Airflow/Prefect, etc.).
• Experience integrating and modeling data from GTM systems such as CRM, marketing automation, billing, and customer success platforms.
• Demonstrated ability to translate business workflows into durable data models that teams can rely on for decision-making.
• Comfortable operating in early-stage environments with messy, incomplete, or inconsistent data.
• Strong cross-functional communication skills and ability to work closely with sales, marketing, CS, finance, and product teams.
• Experience owning architectural decisions for the data stack, including tooling selection and infrastructure design.
• Familiarity with product analytics and event instrumentation across web or application platforms.
• Fluency with AI-assisted development tools such as Cursor, Claude Code, or GitHub Copilot.
• Entrepreneurial mindset with a desire to build and own a data function from the ground up.