Synced from Greenhouse · Jul 2

Manager, Finance Data and AI

DatabricksBengaluru, IndiaPosted Jul 2, 2026
Data EngineerSenior
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Mirrored from Databricks's own Greenhouse careers system · refreshed hourly

870
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Jul 2
Posted
Greenhouse
Applicant system
Job descriptionReq 8604577002

GAQ426R192

About the Role

As Senior Manager, Finance Data and AI, you will lead the team that owns the data foundations, finance semantic layer, financial reporting, and AI-enabled analytics that power Databricks' Finance and Accounting organization. You will report to the Senior Director, Finance Data, AI & Strategy, and serve as the domain and technical anchor for a team that turns financial operations into trusted, well-governed data and reporting. This individual is expected to be based in Bengaluru.

This is a high-impact role at the intersection of finance and accounting domain expertise and modern BI/analytics capability. It is less about deep distributed-systems engineering, and more about deeply understanding how Finance and Accounting operate (close, reconciliations, revenue recognition, chart of accounts) and building a trusted data foundations on top of the Databricks platform and Finance DataLake that Finance and Accounting can rely on every day.

What You Will Do:

  • Orchestrate jobs using Databricks Jobs and Lakeflow Declarative Pipelines, with built-in data validation and reconciliations, to ensure Finance and Accounting can trust the data behind every report
  • Own and evolve the Finance semantic layer: metric definitions, business logic, and data models that ensure a single source of truth for financial reporting across Finance, Accounting, and the broader business
  • Partner closely with Accounting during month-end and quarter-end close, providing hands-on support to ensure reporting data is accurate, timely, and reconciled, and driving timely resolution of data-related issues
  • Build and maintain dashboards and reporting for monthly, quarterly, and executive-level reporting, translating financial data into decision-ready insight for Finance leadership and the CFO organization
  • Identify, scope, and deliver AI use cases that materially change how Finance and Accounting work day to day, including forecasting automation, anomaly detection, and natural language interfaces (Genie Spaces) to financial data
  • Build and maintain internal Finance applications (Databricks Apps, Genie Spaces, dashboards) that enable self-service analytics for non-technical Finance and Accounting stakeholders
  • Publish curated, well-documented finance datasets and metrics, enforcing row-level security and data access policies appropriate for financial data
  • Partner with Accounting, FP&A, Internal Audit, and Procurement to deeply understand business and reporting requirements, and translate them into scalable semantic models and analytics products within the Finance DataLake
  • Manage and grow a team of finance data analysts / finance engineers, setting standards for semantic modeling, dashboard design, and analytics delivery, reviewing work, and developing talent
  • Enforce Git-based version control, change management, and CI/CD practices to satisfy SOX change management requirements for reporting and semantic layer changes
  • Partner with IT and Engineering to provide requirements and perform UAT for new systems and processes affecting Finance data and reporting
  • Establish data management, semantic modeling, and documentation standards and best practices for the Finance organization
  • Serve as a proactive leader who regularly assesses Finance and Accounting pain points, aligns with cross-functional teams on organizational objectives, and holds the team accountable for high-quality, business-relevant results

What We Look For:

  • 12+ years of experience in business intelligence, financial analytics, or analytics engineering, with deep exposure to finance/accounting operations, and at least 3 years in a people management or team lead capacity
  • Strong understanding of core finance and accounting concepts (month-end/quarter-end close, revenue recognition, intercompany, chart of accounts, and financial reporting) and the ability to translate them into semantic models and metrics
  • Proficiency in SQL and Python
  • Experience with BI and data visualization tooling (Tableau, Looker, Power BI, or Databricks AI/BI dashboards), with a track record of building Finance-facing dashboards and self-service reporting products
  • Familiarity with ELT/ETL concepts and how financial source systems (SAP, Salesforce, Stripe, Zuora, or similar) feed into a centralized data lake; direct pipeline-building experience is a plus, not a requirement
  • Demonstrated ability to translate ambiguous Finance requirements into well-scoped, well-governed reporting and analytics deliverables
  • Comfortable working across both technical (Engineering, Data Platform) and non-technical (Accounting, FP&A) stakeholders, translating data concepts for a financial audience and financial concepts for a technical audience
  • Familiarity with AI/ML concepts and enthusiasm for applying them to Finance and Accounting workflows

Nice to Have:

  • Prior experience at a high-growth SaaS or cloud infrastructure company
  • Exposure to 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

Why This Role

You will be embedded in a Finance organization that takes data and AI seriously, reporting directly to the Senior Director of Finance Data, AI & Strategy, with visibility to CFO-level priorities. The team operates with engineering rigor and Finance accountability, and you will have the autonomy to define how the Finance DataLake and modern Finance data infrastructure should evolve at Databricks.

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.

View original posting on Greenhouse

What applying to Databricks usually looks like

Based on publicly available information, candidates applying through greenhouse for roles at Databricks can generally expect a structured process typical of this ATS. This commonly begins with an online application and resume screen, followed by a recruiter conversation to discuss background and role fit. Depending on the position, candidates may encounter technical assessments, take-home exercises, or case studies, particularly for engineering, data, and analytical roles. The process may include multiple stages such as hiring manager conversations, panel discussions, and team or cross-functional interviews. Response times vary and communication is typically managed through the greenhouse platform, including scheduling and status updates. Candidates should prepare to discuss relevant experience, technical skills, and role-specific scenarios, as greenhouse-based processes commonly emphasize structured evaluation criteria across candidates to support consistent, comparative hiring decisions throughout the pipeline.

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Manager, Finance Data and AI
Databricks · Bengaluru, India
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