About the Company
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe.
Responsibilities
- Work with Accounting, FP&A, Internal Audit, and Procurement teams to gather requirements and deliver well-documented technical solutions.
- Develop and maintain ETL pipelines using Databricks Lakehouse and Python/PySpark.
- Build and maintain finance data pipelines in the Finance data lake using Databricks Jobs and Lakeflow Spark Declarative Pipelines.
- Develop and iterate on AI-assisted tools for the Finance and Accounting organisation.
- Contribute to the development of Finance applications (Databricks Apps, Genie Spaces, AI/BI dashboards).
- Build and maintain reports and dashboards for monthly, quarterly, and executive-level reporting.
- Support the implementation of row-level security and data access policies.
- Follow Git-based version control, pull-request review processes, and CI/CD pipelines.
- Support financial close by monitoring pipelines, investigating data issues, and escalating as needed.
- Participate in UAT for new system integrations and assist with technical documentation.
- Contribute to team coding standards and data modelling conventions.
Requirements
- 7+ years of experience in finance and accounting, data engineering, analytics engineering, or finance systems.
- Working knowledge of finance and accounting concepts.
- Proficiency in SQL and Python for pipeline development; hands-on experience with Apache Spark or the Databricks platform is a strong plus.
- Experience connecting to and ingesting from financial source systems (SAP, NetSuite, Salesforce, Stripe, Zuora, or similar).
- Ability to translate Finance requirements into clean, maintainable technical solutions.
- Comfortable communicating across technical and non-technical audiences.
- Experience contributing to BI dashboards and self-service data products.
- Curiosity about AI/ML.
- Nice to have: Prior experience at a high-growth SaaS or cloud infrastructure company, exposure to AI/BI tools, Genie One, or LLM-powered applications.