Key Responsibilities

Delivery &

- Stakeholder Management

- Drive successful delivery of workstream objectives within committed timelines.

- Partner with business stakeholders, architects, product owners, data governance teams, and engineering teams.

- Influence technical and business decisions through data-driven recommendations.

- Proactively identify risks, dependencies, roadblocks and drive resolution.

- Participate in sprint planning, technical design reviews, and release activities.

Databricks Engineering

- Design, develop, optimize, and support scalable data pipelines using Databricks.

- Implement Delta Lake, Delta Live Tables, Workflows, Structured Streaming, and Lakehouse architectures.

- Develop data ingestion, transformation, and consumption solutions.

- Build highly performant Spark applications and optimize workloads for cost and performance.

- Implement CI/CD and DevOps practices for Databricks deployments.

- Support production operations and performance tuning.

Unity Catalog &

- Governance

- Implement and support Unity Catalog migration and adoption.

- Define and manage data governance, access controls, lineage, data classification, and auditing.

- Design enterprise-grade security models using Unity Catalog.

- Enable governed data discovery, sharing, and collaboration across domains.

- Support metadata management, business glossary, domains, metrics, and semantic layers.

Data Sharing &

- Collaboration

- Design and implement secure data-sharing solutions.

- Leverage Delta Sharing, federated access, and cross-platform data integration capabilities.

- Support enterprise collaboration patterns while ensuring compliance and governance requirements.

- Evaluate and implement emerging Databricks data-serving and Lakebase integration capabilities.

AI, Semantic Layer &





- Business Consumption

- Develop and support business-facing semantic models and metric views.

- Enable "Talk to Data" experiences using Databricks Genie.

- Support implementation of Unity Catalog Semantics including:

- Domains

- Business Glossaries

- Metrics

- Semantic Models

- Understand and adopt emerging Databricks capabilities including:

- Genie Ontology

- Genie Agents

- Business Semantics

- AI-powered analytics and consumption patterns

Required Technical Skills

Databricks Core

- Solid hands-on experience with:

- Azure Databricks

- Apache Spark (PySpark preferred)

- Delta Lake

- Databricks Workflows

- SQL Warehouses

- Databricks Asset Bundles

- Delta Live Tables (DLT)

- Structured Streaming

Cloud &

- Data Engineering

- Azure Data Lake Storage (ADLS)

- Azure Data Factory or equivalent orchestration tools

- CI/CD using Azure DevOps or GitHub Actions

- Python, SQL

- Data modeling and dimensional modeling

- Enterprise data warehouse and lakehouse concepts

Unity Catalog &

- Security

- Unity Catalog administration

- Data governance frameworks

- Access control design

- Data lineage and cataloging

- Row and column level security

- Regulatory and compliance considerations

Analytics &

- Semantic Modeling

- Semantic data models

- Metric Views

- Business KPIs and analytical modeling

- Databricks Genie

- Data serving and consumption architectures

Preferred Skills

- Experience in large-scale banking or financial services transformation programs.

- Experience with Delta Sharing and federated data access.

- Understanding of Lakebase architecture and application integration patterns.

- Familiarity with GenAI and Databricks AI capabilities.

- Experience implementing enterprise semantic layers and business ontology models.

- Experience with Agile delivery methodologies.

📌 Databricks (Bengaluru)
🏢 Artech
📍 Bengaluru