Lead Data Engineer - Finance Data Pipelines & AI

Tally; San Francisco (CA); Hybrid; USD 190,000 - 220,000; Full time

Benefits offered by this job

Relocation assistance

Job summary

Happen Bank is seeking a Lead Data Engineer in San Francisco to drive the Financial Data Operations team. You will own critical data pipelines for month-end close, investor reporting, revenue recognition, and governance.

This role bridges engineering, product, and business stakeholders while driving automation and AI-driven improvements. The ideal candidate has 8+ years in data engineering, hands-on AI tooling experience, and strong SQL/data warehouse expertise.

Qualifications

• 8+ years of data engineering experience with leadership exposure.
• Hands-on with AI tools to accelerate work and improve quality.
• Strong SQL, data modeling, and data warehouse fundamentals.

Responsibilities

• Lead a cross-functional scrum team of data engineers across onshore and offshore locations.
• Own architecture and evolution of financial data pipelines supporting GL automation and reporting.
• Partner with finance, accounting, and ops to translate requirements into scalable solutions.
• Drive adoption of Databricks, dbt, Elementary, and Dagster while maintaining production systems.
• Identify opportunities to leverage AI tools for QA, code reviews, and documentation standardization.
• Improve monitoring, alerts, and observability for reliable pipelines.
• Establish coding standards and data quality frameworks across the team.
• Create intelligent runbooks and self-service tools to speed troubleshooting.

Skills

SQL

Data modeling

Data warehousing

Cross-functional collaboration

Mentoring offshore teams

Communication

Education

Bachelor's degree in a related field

Tools

Databricks

dbt

Dagster

Airflow

Spark

PySpark

AWS

S3

Redshift

Job description

Happen Bank is seeking a Lead Data Engineer in San Francisco to drive the Financial Data Operations team. You will own critical data pipelines for month-end close, investor reporting, revenue recognition, and governance.

This role bridges engineering, product, and business stakeholders while driving automation and AI-driven improvements. The ideal candidate has 8+ years in data engineering, hands-on AI tooling experience, and strong SQL/data warehouse expertise.