Job Description :

Technical Skills :

- Deep expertise in Databricks platform (Unity Catalog, Delta Lake, MLflow, Workflows).

- Strong proficiency in PySpark, Scala, or SQL for large-scale data processing.

- Experience designing and building medallion architecture (Bronze/Silver/Gold).

- Hands-on with cloud platforms, preferably Azure.

- Familiarity with CI/CD pipelines and DevOps practices for data engineering.

Architecture & Design :

- Ability to design and implement modern data platforms using Databricks Lakehouse architecture end-to-end.

- Define data architecture, data models, and governance frameworks tailored for enterprise analytics platforms.

- Experience migrating legacy data warehouses (Teradata, Snowflake, etc.) to Databricks.

- Ability to design scalable, cost-optimized data lakehouse solutions.

- Can produce architecture diagrams, technical documentation, and lead design reviews.

Data Modeling & Legacy-to-Cloud Transition :

- Strong data modeling background with hands-on experience in legacy systems such as SQL Server and Oracle.

- Proven track record of transitioning from traditional relational/on-prem data models to modern cloud-native architectures.

- Ability to re-platform legacy schemas, stored procedures, and ETL pipelines into Databricks lakehouse patterns.

- Experience translating dimensional models (star/snowflake schemas) into lakehouse-optimized Delta Lake designs.

- Comfortable bridging the gap between legacy DBA/data modeler roles and modern cloud architecture responsibilities.

Data Governance, Security & Compliance :

- Strong understanding of metadata management, data lineage, data quality, and governance frameworks.

- Hands-on with Databricks Unity Catalog for centralized governance, access control, and lineage tracking.

- Experience enforcing data security and compliance standards, particularly within Financial Services (SOX, PCI-DSS, GDPR).

- Ability to implement role-based access control (RBAC), column/row-level security, and data masking.

- Familiarity with data quality frameworks and tooling integrated within the Databricks ecosystem.

AI & Machine Learning Features (Databricks-Native) :

- Hands-on experience with Databricks Mosaic AI for building, training, and deploying ML models.

- Proficiency with MLflow (native to Databricks) for experiment tracking, model registry, and serving.

- Experience with Databricks Feature Store for creating and managing reusable ML features.

- Ability to build GenAI applications using Databricks Vector Search and Mosaic AI Model Serving.

- Familiarity with Databricks AI/BI and the Genie interface for natural language analytics.

Soft Skills & Collaboration :

- Strong communication skills for working across time zones with onshore teams.

- Able to lead technical discussions, produce architecture diagrams, and document decisions.

- Experience mentoring junior engineers.

Engagement Expectations :

- Willingness to participate in design reviews, sprint planning, and stakeholder calls.

- Deliverable-driven with ability to work independently.

Good to have :

- Good understanding of Agent bricks and Databricks Generative AI capabilities.

Education :

- UG: B.Tech / B.E. in Any Specialization.

Key Skills :

- Skills highlighted with are preferred keyskills: Architect, Azure, Databricks, Artificial Intelligence, SQL, Unity Catalog.