Sr Data Engineer (Databricks)
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Key details
- Location
- Bengaluru, KA, India
- Work style
- Onsite
- Level
- Senior
- Job type
- Full-time
- Experience
- 7–8 yrs
- Function
- Data & ML
- Focus area
- Data engineering & Analytics
- Posted
- 1w ago
Skills
ai/mlazureazure devopsbig dataci/cddata architecturedata engineeringdata modelingdata pipelinesdata qualitydatabricksdelta lakedistributed systemsencryptionetlkafkapower automatepower bipysparkpythonrbacscalasparksql
About the role
Position Overview:
- Lead the design, architecture, and optimization of enterprise-scale data pipelines using Azure Databricks for ACDP projects, driving innovation in DATA & AI initiatives.
- Leverage 7-8 years of expertise to mentor teams, ensure scalability, and deliver mission-critical data solutions.
Key Responsibilities:
- Architect and implement end-to-end data pipelines with Azure Databricks (notebooks, jobs, clusters) for ingestion, ETL/ELT, and complex transformations, incorporating Medallion Architecture (Bronze, Silver, Gold) using Delta Lake for ACID compliance, versioning, schema evolution, and data quality.
- Provide hands-on leadership in Databricks Spark (PySpark, Scala, SQL), Delta Live Tables, Structured Streaming, and Unity Catalog for advanced governance, security, and multi-workspace management.
- Integrate with Azure ecosystem including ADLS Gen2, Azure SQL Database, Serverless/Dedicated SQL Pools; orchestrate via Databricks workflows and hybrid ADF triggers for high-volume ACDP workloads.
- Design and deploy CI/CD pipelines using Azure DevOps, Bicep for infrastructure-as code, and advanced automation for Databricks workspaces, emphasizing zero downtime deployments and cost optimization.
- Champion data quality, validation, cleansing with Databricks expectations/constraints, and compliance via RBAC, encryption, and Purview; mentor on best practices for Photon engine, auto-scaling, spot instances, and performance tuning
- Collaborate with senior analysts, data scientists, and executives to translate complex business requirements into reusable Databricks components, fostering AI/ML integration.
- Drive monitoring, troubleshooting, and optimization for petabyte-scale processing, including unstructured data handling, data modeling, and streaming from Kafka/Event Hubs
Requirements
Required Qualifications:
- 7-8+ years in data engineering, with deep Azure Databricks expertise for big data processing in high-demand environments
- Advanced proficiency in Python/PySpark, SQL, Scala; strong data architecture and distributed systems experience
- Proven track record in cloud-native solutions (AZ-900+ preferred), CI/CD, and mentoring junior engineers
- Excellent communication for stakeholder alignment and documentation
Preferred: Power BI, Power Automate, Azure ML, enterprise governance tools.
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