Databricks Engineer

Key Responsibilities:

• Pipeline Development Design, implement, and optimize scalable ETL/ELT pipelines using Databricks (PySpark, SQL, Delta Lake) to ingest structured and semi structured data from multiple sources (APIs, databases, streaming).
• Data Warehousing Build and maintain cloud data warehouse solutions (Snowflake / Azure Synapse) design star schemas, fact/dimension tables, and aggregate tables for high performance reporting.
• Data Modeling Create logical and physical data models for operational and analytical use cases; implement SCD Type 2, slowly changing dimensions, and data vault methodologies where appropriate.
• Performance Tuning Optimize Spark jobs, SQL queries, and data partitioning strategies to handle petabyte scale data with low latency.
• Governance & Quality Implement data quality checks, monitoring, and lineage using tools like Great Expectations or custom frameworks; enforce data governance policies (GDPR/CCPA).
• Collaboration Partner with data analysts, product managers, and engineers to translate business requirements into technical data solutions.
• CI/CD & Automation Automate deployment of data pipelines using Azure DevOps or GitHub Actions; maintain infrastructure as code (Terraform) for data resources.

• Total Experience: 10+ years in data engineering or related roles.
• Cloud Data Platforms: Deep hands on experience with Databricks (notebooks, jobs, clusters, Delta Lake, Unity Catalog) must have production level work.
• Data Warehousing: Proven experience with cloud data warehouses (Snowflake, Azure Synapse, or Redshift) design, optimisation, and administration.
• Data Modeling: Strong knowledge of dimensional modeling (Kimball/Inmon), relational database design, and experience with tools like ER/Studio or dbt.
• Programming: Expert in Python and SQL ability to write maintainable, production grade code.
• Big Data: Hands on with Apache Spark (PySpark), distributed computing, and performance tuning.
• Orchestration: Experience with workflow tools (Airflow, Azure Data Factory, or Prefect) for scheduling and monitoring pipelines.
• Version Control: Proficient with Git and collaborative development workflows.

• Experience with streaming technologies (Kafka, Event Hubs, or Kinesis).
• Knowledge of data mesh or data fabric architectures.
• Familiarity with BI tools (Power BI, Tableau, Looker).
• Databricks certification (e.g., Associate or Professional Data Engineer).
• Experience with dbt (data build tool) and transformation testing.
• Exposure to MLflow or MLOps practices.

• Bachelor's or Master's degree in Computer Science, Information Systems, or a related field (or equivalent practical experience).
• Strong communication skills
• Self starter with a problem solving mindset and ability to work independently in a hybrid environment.

For applications and inquiries,