Data Engineer

JD:-

We are looking for an experienced Data Engineer with strong hands-on expertise in Databricks, PySpark, Python, and SQL. The candidate will be responsible for designing, developing, and optimizing scalable data pipelines and data-processing solutions in a cloud-based data environment.

The candidate should be a hands-on Data Engineer who has personally built and supported Databricks solutions, rather than someone with only theoretical exposure. They should be able to explain real examples of PySpark transformations, ETL/ELT pipelines, Delta Lake implementations, performance optimization, data-quality controls, and production troubleshooting.

Key Skills:- Databricks | PySpark | Python | SQL | ETL/ELT | Delta Lake | Spark | Cloud | Data Engineering.

Responsibilities

• Design and develop scalable ETL/ELT data pipelines using Databricks.
• Develop data-processing and transformation solutions using PySpark and Python.
• Write complex SQL queries for data transformation, validation, and analysis.
• Build and maintain solutions using Delta Lake and Lakehouse architecture.
• Integrate data from multiple sources, including databases, APIs, files, and cloud storage.
• Optimize Spark jobs, Databricks workloads, and SQL queries for performance and cost efficiency.
• Implement data-quality, validation, reconciliation, monitoring, and error-handling mechanisms.
• Troubleshoot production data issues and perform root-cause analysis.
• Collaborate with business analysts, architects, and application teams to translate business requirements into technical solutions.
• Follow engineering best practices for testing, source control, CI/CD, documentation, and production deployment.

Required Skills & Experience

• 5–8 years of overall Data Engineering experience.
• Strong hands-on experience with Databricks.
• Strong hands-on development experience using PySpark / Apache Spark.
• Strong Python programming skills and advanced SQL skills.
• Experience developing production-grade ETL/ELT pipelines.
• Experience with Delta Lake and Lakehouse concepts.
• Experience processing large-volume and complex datasets.
• Good understanding of data modeling, data quality, and data governance principles.
• Strong troubleshooting and performance-tuning skills.
• Experience with at least one cloud platform: Microsoft Azure, AWS, or Google Cloud.
• Experience with Git and CI/CD practices.

Good to Have Skills

• Databricks Unity Catalog experience.
• Databricks Workflows / Jobs and cluster optimization experience.
• Experience with Azure Data Factory or Apache Airflow.
• Experience with Kafka or Spark Structured Streaming.
• Knowledge of cloud-native storage and data services.
• Databricks or cloud certification.
• Experience in banking or financial-services data environments.