[Hiring] Senior Data Engineer @Lean Solutions Group

Role Description

We are hiring a part-time Data Engineer to maintain and extend our production dbt project on Snowflake, which integrates data from DealCloud, SourceScrub, People Data Labs, Tracxn, and proprietary feeds to power analytics, dashboards, and investment decision-making across the firm. This is a full-ownership role, not a support or junior position. You will own assigned model domains end-to-end, from design through production monitoring, working within a complex medallion-architecture project (600+ staging views, 168 intermediate models, 120+ mart models). The repo also includes AI powered models, both SQL (Snowflake Cortex) and Python, that call LLMs for tasks like entity tagging and enrichment.

Averaging 20 hours per week, with potential to scale up during migrations or when new data sources are onboarded. Our team is in FTV's New York office Monday–Thursday, but we're open to this role being remote.

What you will be doing:

• Core Data Engineering:
  • Build and maintain staging, intermediate, and mart models across the medallion architecture (Bronze → Silver → Gold)
  • Design and own cross-source entity resolution — ID and bridge logic for companies, contacts, and deals — not just consume existing spines
  • Maintain and extend AI-powered tagging (Snowflake Cortex) — including prompt design for AI generated company fields and taxonomy consolidation
  • Own data quality through comprehensive testing — schema validation, row-count checks, nullability and uniqueness constraints
  • Optimize query performance and manage materializations (views, tables, incremental models) for production workloads
• Source System Integration:
  • Maintain source-system bridges and lookup tables for enum fields across DealCloud, SourceScrub, PDL, Tracxn, and other feeds
  • Adapt models when upstream systems change field names, types, or structure
  • Keep source YAML documentation current, lineage, freshness expectations, and schema notes
• Maintenance & Review:
  • Support ad-hoc requests from data team members
  • Review and merge internal team pull requests; enforce naming conventions and architectural patterns
  • Troubleshoot production incidents, debug failed runs, investigate data inconsistencies, coordinate fixes
  • Monitor dbt Cloud jobs and catch incremental model or pipeline failures

Qualifications

• 3+ years of data engineering experience in a production environment

Requirements

• Technical:
  • Expert SQL, complex multi-stage CTEs, window functions, performance-optimized queries
  • dbt expertise, models, tests, sources, macros, and materializations
  • Snowflake or similar cloud data warehouse (BigQuery, Redshift, Databricks)
  • Dimensional modeling and medallion/layered architectures
  • Git and GitHub workflows, branching strategies, and code review
  • Automated testing, SLA monitoring, and validation framework design
• Nice to have:
  • Python for data validation or transformation logic
  • dbt macros and Jinja templating
  • Experience with investment/financial data or alternative data providers
• Soft Skills:
  • Ownership mentality, takes pride in data quality, not just tickets closed
  • Rigor and attention to detail, writes tests alongside models, documents work, cares about naming conventions
  • Curiosity, investigates anomalies, digs into source schemas, improves documentation
  • Pragmatism, balances perfection with shipping; knows when to refactor vs. move forward
  • Comfortable working independently in a part-time, flexible-hours arrangement

Benefits

• Join a powerful tech workforce and help us change the world through technology
• Professional development opportunities with international customers
• Collaborative work environment
• Career path and mentorship programs that will lead to new levels