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# Lead Data Engineer

- **Company:** NA MAKE IT HAPPEN LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $190,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Accounting Systems, Artificial Intelligence, Airflow, Amazon S3, Automation of Tests, Code Review, Data Validation, Information Engineering, Data Governance, Data Infrastructure, Data Transformation, Data Warehousing, Distributed Computing Environment, Apache Oozie, Operational Databases, Performance Tuning, Scrum Methodology, Site Reliability Engineering Practices, DataOps, SQL Databases, Video Encoding, Workflow Management Systems, GitHub Copilot, Snowflake, Apache Spark, Pyspark, Streamlit Framework, GPT, Data Pipelines, Databricks
- **Published:** July 26, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0c4574eac712b6f2

## About the Role

* 8+ years of experience in data engineering, with 2+ years leading teams or projects in a technical lead capacity; bachelor's degree in a related field; or equivalent work experience
 * You have hands-on experience using AI tools to accelerate your work and improve output quality - you're equally comfortable using them yourself and showing colleagues how, and you're thoughtful about limitations and where human judgment matters most
 * Strong expertise in SQL, data modeling, data warehouse concepts, and building production data pipelines at scale
 * Experience with orchestration tools such as Airflow, Oozie, or Dagster and distributed processing frameworks like Spark or PySpark
 * Working knowledge of AWS services (EMR, S3, Redshift) and modern data platforms such as Snowflake or Databricks
 * You take ownership of outcomes, proactively identifying risks and workflow improvements before they become blockers
 * Strong communication and organizational skills with experience collaborating across business, product, and engineering teams
 * Experience leading or mentoring offshore engineering teams and managing work across time zones
 * You balance technical depth with business context, understanding how data pipelines support financial processes and regulatory requirements, * Experience with dbt for data transformation and Elementary for data validation
 * Hands-on use of AI tools such as Claude, ChatGPT, or GitHub Copilot to improve development workflows
 * Background in financial services, accounting systems, or investor reporting processes
 * Experience building self-service analytics tools using Streamlit or similar frameworks
 * Familiarity with data quality frameworks, alerting systems, and SRE practices for data infrastructure

## Description

Happen Bank's Data Operations Center ensures consistent, reliable delivery of data that powers critical business functions across finance, accounting, investor reporting, and collections. As a Lead Data Engineer, you'll lead the Financial Data Operations team, owning the pipelines that support month-end close, investor servicing, revenue recognition, and other high-impact processes that keep the business running smoothly. You'll act as the bridge between engineering, product, and business stakeholders while building team capability and driving operational excellence through smart automation and AI-driven improvements., * Lead a cross-functional scrum team of onshore and offshore data engineers, setting priorities and removing blockers to deliver reliable data products

 * Own the architecture, maintenance, and continuous improvement of critical financial data pipelines that support GL automation, investor reporting, tax documents, and collections workflows
 * Partner with finance, accounting, and operations stakeholders to translate business requirements into scalable technical solutions
 * Drive adoption of modern data platform capabilities, including Databricks, dbt, Elementary, and Dagster, while maintaining existing production systems
 * Identify opportunities to leverage AI tools for QA automation, code review, performance optimization, and documentation standardization
 * Build and improve monitoring, alerting, and observability practices to ensure pipeline reliability and rapid incident response
 * Establish coding standards and data quality frameworks that scale across the team and reduce operational overhead
 * Use AI to create intelligent runbooks, diagnostic agents, and self-service tools that empower L1 support and accelerate troubleshooting, For select roles and locations, candidate interviews may be recorded, transcribed and summarized by tools such as artificial intelligence (AI) to assist our hiring managers with the application process.

You will have the opportunity to opt out of recording, transcription, and summarization prior to any scheduled interviews. We will not discriminate against you if you choose to opt out.

During the interview, we will collect the following categories of personal information from or about you: contact information, identifiers, professional and employment-related information, sensory information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment.


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