About the role

Lead Data Engineer building reliable data infrastructure at Eve, a legal technology company. Architecting data systems to enable data-driven decision making for law firms.

Responsibilities

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.

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**.