Data Engineer

Data Engineer

Location: United States Job Type: Full-Time | Exempt | Hybrid/Remote Eligible Salary Range: $115,000 - $140,000 per year

About Commercial Bank of California

Commercial Bank of California (CBC) is the largest Latino-owned bank in California and a certified Minority Depository Institution (MDI). Headquartered in Irvine and founded in 2003, CBC is one of the largest privately held banks in the state, with over $4 billion in assets as of November 2026.

We are a purpose-driven financial institution committed to building long-term relationships and delivering innovative, personalized banking solutions. Our leadership reflects the diverse communities we serve, and our mission is rooted in empowering entrepreneurs, business owners, and community leaders to thrive.

As a certified MDI, CBC is proud to play a vital role in advancing financial inclusion and economic opportunity. We believe in a higher vision for banking-one that prioritizes trust, collaboration, and community impact. Join us and be part of a team that's redefining what it means to be a community-focused, relationship-driven bank that puts people over profits.

Job Summary

The Data Engineer builds, maintains, and supports reliable, secure data pipelines and analytical data stores that power enterprise analytics, reporting, and AI use cases. With 3-6 years of hands-on data engineering experience, this role works within established architecture standards, partnering with other team members to deliver and operate solutions across the Azure ecosystem.

You will contribute to implementations on Microsoft Fabric, Azure Databricks, and modern lakehouse architectures, and help support the migration of Databricks workloads to Microsoft Fabric, while working with business intelligence, analytics, AI, and business stakeholders. This role emphasizes data quality, reliability, and continuous learning.

Essential Duties and Responsibilities

Data Platform & Pipelines

• Develop, test, and maintain batch and streaming data pipelines using Databricks, Fabric Data Pipelines, and Azure-native services. • Implement ETL/ELT processes that ensure data quality, accuracy, and consistency across systems. • Build and maintain lakehouse and medallion (Bronze/Silver/Gold) data layers following team standards. • Support Databricks-to-Fabric migration work when and where applicable, including converting notebooks and pipelines, and validating results. • Monitor pipelines, troubleshoot failures, and help tune for performance, scalability, and cost efficiency. • Integrate and model data from FIS core banking and H360 systems into the lakehouse for analytics and reporting. • Support pipelines for payments data (e.g., ACH, wire, card, and real-time payments), including ingestion, reconciliation, and reporting. • Design, build, and maintain enterprise-grade data platforms using Microsoft Fabric, Azure Data Lake, Azure Synapse, and Azure Databricks. • Architect and implement lakehouse and medallion architectures (Bronze/Silver/Gold) to support analytics and BI workloads. • Define standards for data ingestion, transformation, storage, and access across structured and semi-structured data sources. • Microsoft expertise across the data and analytics stack (Fabric, Azure Data Lake, Synapse, Power BI, Microsoft Purview). • Plan and execute Databricks-to-Fabric migrations, including workload assessment, Delta/OneLake data movement, notebook and pipeline conversion, semantic model re-pointing, validation, and cutover where applicable.