Source description
About the role
We are seeking a highly skilled Databricks Data Engineer to design, build, and optimize scalable data pipelines and lakehouse architectures. The ideal candidate will have strong expertise in Apache Spark, PySpark, Delta Lake, and modern cloud-based data platforms, with the ability to transform complex business requirements into reliable and performant data solutions.
Location : All Brillio Location:
Experience:4 to 8yrs
🔧 Key Responsibilities
- Data Engineering & Pipeline Development Design and develop scalable ETL/ELT pipelines using PySpark and Spark SQL Build and maintain Databricks workflows (Jobs) for orchestration Implement Delta Live Tables (DLT) for declarative pipeline development Develop and manage batch and streaming data pipelines
- Lakehouse Architecture Implementation Design and implement Medallion Architecture (Bronze, Silver, Gold layers) Build curated datasets for analytics and reporting Optimize storage using Delta Lake best practices
- Data Ingestion & Integration Ingest data from multiple sources: Databases (RDBMS, NoSQL) APIs and streaming platforms (Kafka, Event Hubs) Files (CSV, JSON, Parquet) Handle structured and semi-structured data efficiently
- Delta Lake & Performance Optimization Implement: ACID transactions Schema enforcement and evolution Change Data Capture (CDC) Optimize Spark jobs using: Partitioning strategies Caching and broadcast joins File compaction and indexing (Z-ORDER)
- Data Quality & Governance Implement data validation and quality checks Ensure compliance with data governance standards Use Unity Catalog for access control and data lineage Maintain auditability and data traceability
- Monitoring & Reliability Build logging, monitoring, and alerting for pipelines Troubleshoot failures and optimize performance Ensure high availability and fault tolerance
- Collaboration & Delivery Work closely with Data Analysts, Data Scientists, and stakeholders Translate business requirements into data models and pipelines Participate in Agile ceremonies (Sprint planning, stand-ups, retrospectives) ✅ Required Qualifications Bachelor’s degree in Computer Science, Engineering, or related field 4–8+ years of experience in Data Engineering Strong hands-on experience with: Databricks platform PySpark and Spark SQL Delta Lake Experience with: ETL/ELT pipeline development Distributed data processing Solid understanding of: Data modeling (Star/Snowflake schemas) Data warehousing concepts ⭐ Preferred Qualifications Experience with Delta Live Tables (DLT) Knowledge of CI/CD pipelines (Azure DevOps, GitHub Actions) Experience with streaming frameworks (Kafka, Spark Streaming) Familiarity with cloud platforms: Azure / AWS / GCP Experience with MLflow and MLOps workflows Domain experience (e.g., Healthcare, Finance, Retail) 🛠️ Technical Skills Languages: Python, SQL Frameworks: Apache Spark Tools: Databricks, Delta Lake, MLflow Data Formats: Parquet, Delta, JSON, Avro Orchestration: Databricks Workflows / Airflow Version Control: Git 💡 Soft Skills Strong problem-solving and analytical thinking Excellent communication skills Ability to work in a collaborative environment Attention to detail and data quality 🚀 Nice-to-Have (Optional Add-ons) Experience with real-time analytics Exposure to data governance tools Certification in Databricks or Cloud platforms
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