Mid-level Data Engineer

Role overview A boutique data and AI consultancy is hiring a mid-level Data Engineer to support the analytics platform of a growth equity investment firm. The position centers on a large, production-scale dbt project running on Snowflake that unifies CRM, market intelligence, and proprietary data sources to inform investment decisions. It is an execution-driven role within a layered medallion architecture, where a senior engineer defines the design and the engineer builds, tests, and maintains the models.

Responsibilities - Build and maintain staging, intermediate, and mart dbt models within a medallion architecture spanning hundreds of views and models. - Implement entity resolution logic, including ID and bridge tables for companies, contacts, and deals, following senior-engineer designs. - Develop and maintain AI-powered tagging models using Snowflake Cortex, surfacing data-quality concerns for review. - Write and run tests on every model touched, covering schema validation, row counts, nullability, and uniqueness. - Maintain source-system bridges, lookup tables, and YAML documentation such as lineage, freshness, and schema notes. - Monitor dbt Cloud jobs, troubleshoot failed runs, and investigate data inconsistencies, escalating when necessary. - Submit clean, well-documented pull requests for senior code review.

Requirements - 2–4 years of data engineering experience in a production environment. - Strong SQL skills, including multi-stage CTEs, window functions, and query optimization. - Working knowledge of dbt, including models, tests, sources, and basic macros. - Hands-on experience with Snowflake or a comparable cloud data warehouse such as BigQuery, Redshift, or Databricks. - Familiarity with dimensional modeling and layered medallion-style architectures. - Proficiency with Git and GitHub workflows, including branching, pull requests, and code review. - Strong written English and reliable asynchronous communication with a remote US-based team. - Minimum of 20 hours per week, with at least 4 hours of overlap with US Eastern time, Monday through Thursday.

Nice to have - Python experience for data validation or transformation logic. - dbt macros and Jinja templating skills. - Exposure to investment, financial, or alternative data.

Benefits and work setup - Fully remote with a flexible part-time schedule. - Mentorship from senior data engineers and a clear path to expanded ownership. - Unlimited paid time off and paid parental and bereavement leave. - Opportunity to work across diverse, challenging projects for globally recognized clients.