Databricks Unified Data Analytics Platform Engineer

• Minimum 1 year of experience is required
• Develop high-quality, scalable ETL/ELT pipelines using Databricks technologies including Delta Lake, Auto Loader, and DLT.
• Excellent programming and debugging skills in Python.
• Strong hands-on experience with PySpark to build efficient data transformation and validation logic.
• Must be proficient in at least one cloud platform: AWS, GCP, or Azure.
• Create modular dbx functions for transformation, PII masking, and validation logic — reusable across DLT and notebook pipelines.
• Implement ingestion patterns using Auto Loader with checkpointing and schema evolution for structured and semi-structured data.
• Build secure and observable DLT pipelines with DLT Expectations, supporting Bronze/Silver/Gold medallion layering.
• Configure Unity Catalog: set up catalogs, schemas, user/group access, enable audit logging, and define masking for PII fields.
• Enable secure data access across domains and workspaces via Unity Catalog External Locations, Volumes, and lineage tracking.
• Access and utilize data assets from the Databricks Marketplace to support enrichment, model training, or benchmarking.
• Collaborate with data sharing stakeholders to implement Delta Sharing — both internally and externally.
• Integrate Power BI/Tableau/Looker with Databricks using optimized connectors (ODBC/JDBC) and Unity Catalog security controls.
• Build stakeholder-facing SQL Dashboards within Databricks to monitor KPIs, data pipeline health, and operational SLAs.
• Prepare GenAI-compatible datasets: manage vector embeddings, index with Databricks Vector Search, and use Feature Store with MLflow.
• Package and deploy pipelines using Databricks Asset Bundles through CI/CD pipelines in GitHub or GitLab.
• Troubleshoot, tune, and optimize jobs using Photon engine and serverless compute, ensuring cost efficiency and SLA reliability.
• Experience with cloud-based services relevant to data engineering, data storage, data processing, data warehousing, real-time streaming, and serverless computing.
• Hands on Experience in applying Performance optimization technique
• Understanding data modeling and data warehousing principles is essential

​Good to Have:

• Certifications: Databricks Certified Professional or similar certifications.
• Machine Learning: Knowledge of machine learning concepts and experience with popular ML libraries.
• Knowledge of big data processing (e.g., Spark, Hadoop, Hive,Kafka)
• Data Orchestration: Apache Airflow.
• Knowledge of CI/CD pipelines and DevOps practices in a cloud environment.
• Experience with ETL tools like Informatica, Talend, Matillion, or Fivetran.
• Familiarity with dbt (Data Build Tool)

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