Key Responsibilities

- Design, develop, and maintain scalable ETL/ELT data pipelines using Python and PySpark.
- Work extensively with Databricks and Delta Lake, including Delta tables, MERGE operations, schema evolution, Time Travel, and data optimization.
- Develop, schedule, and monitor Apache Airflow DAGs for workflow orchestration and dependency management.
- Work with Kubernetes for containerized application deployment, orchestration, monitoring, and troubleshooting.
- Develop reusable and optimized Python code following software engineering best practices.
- Optimize Spark jobs for performance, scalability, and cost efficiency.
- Implement data quality checks, error handling, logging, and monitoring mechanisms.
- Use Git for version control, branching, pull requests, code reviews, and collaborative development.
- Troubleshoot pipeline failures, investigate data issues, and implement reliable solutions.
- Collaborate with cross-functional teams to understand business requirements and deliver efficient data solutions.

Required Technical Skills

- Python: Strong programming skills, including data manipulation, exception handling, modular programming, and debugging.
- PySpark: Hands-on experience with Spark DataFrames, Spark SQL, joins, transformations, partitioning, and performance tuning.
- Databricks: Strong understanding of Databricks architecture, notebooks, workflows, compute management, and job execution.
- Delta Lake: Experience with Delta tables, ACID transactions, MERGE/UPSERT, schema enforcement, schema evolution, VACUUM, and Time Travel.
- Apache Airflow:



Experience developing DAGs, managing task dependencies, configuring retries, scheduling workflows, and troubleshooting failures.
- Kubernetes: Practical knowledge of pods, deployments, services, ConfigMaps, Secrets, resource management, and troubleshooting containerized workloads.
- Git: Proficiency in branching strategies, pull requests, merging, and resolving conflicts.
- SQL: Strong SQL skills, including complex joins, CTEs, window functions, and query optimization.

Good to Have

- Experience with Docker and containerization best practices.
- Experience with Databricks Unity Catalog and data governance.
- Knowledge of CI/CD pipelines and automated deployments.
- Experience deploying and managing Airflow workloads on Kubernetes.
- Familiarity with Kubernetes monitoring, autoscaling, and resource optimization.
- Experience with cloud platforms such as Azure, AWS, or GCP.
- Understanding of data lakehouse architecture and data modeling.
- Experience with batch and incremental data processing.
- Familiarity with data quality frameworks and automated testing.

Preferred Candidate Profile

- Solid analytical and problem-solving skills.
- Ability to independently design, develop, deploy, and troubleshoot data pipelines.
- Good understanding of distributed data processing and container orchestration.
- Experience working in an Agile development environment.
- Strong communication and collaboration skills.
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Skills:- Python, databricks, Apache Airflow, PySpark, Delta Lake and Kubernetes

📌 Python Data Engineer (Pune)
🏢 wissen technology
📍 Pune