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Azure Databricks, Genie
Job Description
- Primary skills: Azure Databricks, Genie
- Lead end-to-end implementation of data engineering solutions on Azure Databricks, ensuring scalability, reliability, and maintainability.
- Drive technical design discussions and translate business requirements into well-structured Databricks/Genie-based solutions.
- Provide technical guidance, code reviews, and mentorship to ensure consistent engineering standards across the team. Databricks & Genie Development
- Build and optimize notebooks, jobs, and workflows leveraging Azure Databricks and Genie capabilities.
- Develop reusable components and patterns to accelerate delivery across multiple use cases.
- Troubleshoot production issues, perform root-cause analysis, and implement preventive improvements. Performance, Quality & Operations
- Optimize cluster configurations, job performance, and resource usage to balance speed and cost.
- Establish monitoring and operational practices for pipeline health, failures, and SLAs.
- Ensure data quality checks and validation steps are embedded into pipelines and workflows. Minimum Qualifications:
- 5–8 years of overall experience in data engineering / analytics engineering roles with ownership of production-grade delivery.
- Strong hands-on experience with Azure Databricks and Genie for building and managing data workflows.
- Solid experience with Databricks development and operationalization (jobs, workflows, notebooks).
- Ability to lead technical discussions, perform reviews, and guide implementation best practices.
- Education: BTECH, MTECH, MCA, MSC. Preferred Qualifications:
- Experience designing scalable data processing patterns and reusable frameworks within Databricks environments.
- Proven track record of performance tuning and cost optimization for Databricks workloads in enterprise settings.
- Experience establishing engineering standards (branching, reviews, release practices) and mentoring team members.
- Strong stakeholder management skills with the ability to align technical delivery to business outcomes.
- Exposure to production support models, incident handling, and continuous improvement for data platforms.
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Key Skills
About Company
Skills:
Pyspark, Version Control, Azure Databricks, Data Modeling, Data Warehousing, Databricks SQL
Skills:
snowflake , Prometheus, Kafka, Datadog, Sql, ELT, Azure Synapse, Azure Data Factory, Terraform, Docker, Spark, Databricks, Python, Kubernetes, Etl, Airflow, MLflow, CI CD, Azure AI Foundry, dbt, Microsoft Fabric, Bicep, Azure Monitor
Skills:
Azure Storage, Azure Databricks, Apache Airflow, Python, Github, Pyspark, Spark, Cycode, CodeQL