Finance Data and AI Lead

Databricks
Bengaluru
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningExperience: 12+ yearsSkills: ["Technical leadership","Mentorship","Architectural decision-making","Stakeholder management","Problem-solving"]

Lead Finance Data and AI engineering by architecting, building, and owning complex finance data pipelines in the Finance DataLake. Drive AI use cases for forecasting automation, anomaly detection, and natural language access to financial data. Create self-service Finance applications and curated, governed datasets with Unity Catalog and row-level security. Partner with Accounting, FP&A, Internal Audit, and Procurement, own close-critical data accuracy, and mentor engineers while evolving standards and CI/CD for SOX change management.

Loading

Loading job details...

Preparing the role view and application actions.

FursaFursa
Databricks
Databricks
2 months ago

Finance Data and AI Lead

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 13 hours agoStatus: Live

Job Summary

Lead Finance Data and AI engineering by architecting, building, and owning complex finance data pipelines in the Finance DataLake. Drive AI use cases for forecasting automation, anomaly detection, and natural language access to financial data. Create self-service Finance applications and curated, governed datasets with Unity Catalog and row-level security. Partner with Accounting, FP&A, Internal Audit, and Procurement, own close-critical data accuracy, and mentor engineers while evolving standards and CI/CD for SOX change management.
Location: Bengaluru
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Sr. Manager level

Key Responsibilities

  • •Architect, build, and own the most complex finance data pipelines in the Finance DataLake using Databricks Jobs and Lakeflow Declarative Pipelines with validation and reconciliations.
  • •Lead technical design of AI use cases for Finance and Accounting, including forecasting automation, anomaly detection, and natural language interfaces to financial data.
  • •Build and maintain internal Finance applications (Databricks Apps, Genie Spaces, dashboards) enabling self-service for non-technical Finance stakeholders.
  • •Design and deliver curated Finance DataLake datasets while enforcing row-level security, data access policies, and Unity Catalog governance.
  • •Serve as a primary technical point of contact during financial close, ensure data accuracy, and resolve pipeline issues; mentor engineers and evolve standards and CI/CD to meet SOX change management needs.

Key Requirements

  • •12+ years of experience in data engineering, analytics engineering, or finance systems, owning end-to-end production-grade pipelines.
  • •Deep proficiency in SQL and Python with hands-on experience using Apache Spark and the Databricks platform.
  • •Build and maintain ELT/ETL pipelines from financial source systems such as NetSuite, Salesforce, Stripe, and Zuora into a centralized data lake.
  • •Strong understanding of core finance/accounting concepts including close processes, revenue recognition, intercompany, chart of accounts, and financial reporting.
  • •Independently translate ambiguous Finance requirements into well-architected, maintainable technical solutions while driving technical discussions with Finance and engineering stakeholders.
Experience:12+ yearsFinance systemsData engineeringAnalytics engineering
Skills:Technical leadershipMentorshipArchitectural decision-makingStakeholder managementProblem-solving
Languages:English
Tech Stack:SQLPythonApache SparkDatabricks platformDatabricks JobsLakeflow Declarative PipelinesDatabricks AppsGenie SpacesUnity CatalogRow-level securityGitHub ActionsGit-based version controlCI/CDDeclarative Automation BundlesELT/ETLData lakeNetsuiteSalesforceStripeZuora

Company Brief

Databricks
Provides a unified data analytics platform powered by Apache Spark to simplify building, deploying, and scaling data engineering, data science, and machine learning workloads for enterprises.
Industry: Data Infrastructure
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Scaleup
Valuation: Unicorn (USD 1B+)
Funding: Series E+
Headquarters: San Francisco, United States
Founded: 2013
WebsiteLinkedIn