Level

Databricks

Finance Data and AI Lead

Databricks is hiring a Finance Data and AI Lead in Bengaluru, India. Level rates it ; you can apply on Databricks.

Level's read on this job

  • Level rates this finance role Works on AI ●●●○: the daily work is on AI products, without building the model.
  • Tools and skills the posting cites include Python, PySpark and Databricks SQL.
  • Level classifies it as a lead role.
  • Level lists 894 other open roles at Databricks.

AI in this role

Lead data pipelines and AI systems development, architecting financial automation, anomaly detection, and NLP interfaces on Databricks.

pythonpysparkdatabricks-sqlgithub-actions
etldata-pipelinesfinancial-engineeringmachine-learning-systemsdata-governance

GAQ327R175

About the Role

This role is a part of the Finance Organisation.

As Finance Data Lead on the Finance Data and AI team, you will be the technical authority behind the data pipelines, AI systems, and internal applications that power Databricks' Finance and Accounting organisation. You will report to the Senior Manager, Finance Data and AI and serve as the individual contributor who sets the technical bar, drives architectural decisions, and delivers the highest-complexity work on the team. This individual is expected to be based in Bengaluru.

This is a high-impact role at the intersection of finance domain expertise and data platform capability. You will work closely with a team of finance data engineers as a technical lead and mentor, and partner directly with Accounting, FP&A, and Finance leadership to define what gets built and how.

What You Will Do:

  • Design and develop ETL pipelines using Databricks SQL and Python / PySpark to enhance reporting, automate journal entries, and transform core financial processes across various domains in accounting and FP&A, such as revenue, expenses, equity, commissions, and tax
  • Architect, build, and own the most complex finance data pipelines in the Finance data lake using Databricks Jobs and Lakeflow Declarative Pipelines with built-in data validation and reconciliations
  • Lead the technical design of AI use cases for the Finance and Accounting organisation, including forecasting automation, anomaly detection, and natural language interfaces to financial data
  • Build and maintain internal Finance applications (Databricks Apps, Genie Agents, AI/BI dashboards) that enable self-service for non-technical Finance stakeholders
  • Design and deliver curated Finance datasets and enforce row-level security, data access policies, and Unity Catalog governance standards
  • Define and champion coding standards, data modelling conventions, documentation practices, and testing frameworks across the Finance engineering team
  • Enforce and evolve Git-based version control, pull-request review processes, and CI/CD pipelines (Declarative Automation Bundles, GitHub Actions) to satisfy SOX change management requirements
  • Lead technical scoping and solutioning for requirements from Accounting, FP&A, Internal Audit, and Procurement teams
  • Serve as the primary technical point of contact during financial close, ensuring data accuracy and timely resolution of pipeline issues
  • Partner with IT and Engineering on new system integrations, providing detailed technical requirements and leading UAT
  • Mentor junior and mid-level engineers through code reviews, pairing, and design discussions, raising the technical quality of the broader team
  • Proactively identify architectural debt, performance bottlenecks, and tooling gaps, and drive resolution with minimal direction

What We Look For:

  • 12+ years of experience in data engineering, analytics engineering, or finance systems, with a track record of owning complex, production-grade pipelines end-to-end
  • Deep proficiency in SQL and Python; hands-on experience with Apache Spark and the Databricks platform
  • Experience building and maintaining ELT/ETL pipelines from financial source systems (NetSuite, Salesforce, Stripe, Zuora, or similar) into a centralised data lake
  • Strong understanding of core finance and accounting concepts, including close processes, revenue recognition, intercompany, chart of accounts, and financial reporting
  • Ability to independently translate ambiguous Finance requirements into well-architected, maintainable technical solutions
  • Comfortable driving technical conversations with both engineering peers and non-technical Finance stakeholders
  • Experience building Finance-facing dashboards, self-service BI products, and executive reporting layers
  • Familiarity with AI/ML concepts with demonstrated enthusiasm for applying them to Finance workflows

Nice to Have

  • Prior experience at a high-growth SaaS or cloud infrastructure company
  • Hands-on experience with AI/BI tools, Genie, or LLM-powered applications
  • Experience with Declarative Automation Bundles or CI/CD for Finance DataLake pipelines
  • CPA, CFA, or formal finance/accounting background

About Databricks

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, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

How we rate this

Finance Data and AI Lead at Databricks rates 75 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ETLData PipelinesFinancial EngineeringMachine Learning SystemsData GovernancePythonPySparkDatabricks SQL

Also common in finance jobs at this level

Pay

Typical finance job
$107k

This posting doesn't list pay. Use the typical finance figure when pay comes up. See finance jobs

Questions you could be asked

  1. Tell me about a project where etl was part of your work. What did you do?
  2. Tell me about a project where data pipelines was part of your work. What did you do?
  3. Tell me about a project where financial engineering was part of your work. What did you do?
  4. Tell me about a project where machine learning systems was part of your work. What did you do?
  5. Tell me about a project where data governance was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: ETL, Data Pipelines, Financial Engineering, Machine Learning Systems, and Data Governance. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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