Job description

Key Responsibilities • Data Pipeline Development: Design and implement robust ETL/ELT pipelines using Databricks, PySpark, and Delta Lake to process structured and unstructured data efficiently. • Performance Optimization: Tune and optimize Databricks clusters and notebooks for performance, scalability, and cost-efficiency. • Collaboration: Work closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions that meet business needs. • Cloud Integration: Leverage cloud platforms (AWS, Azure, GCP) to build and deploy data solutions, ensuring seamless integration with existing infrastructure. • Data Modeling: Develop and maintain data models that support analytics and machine learning workflows. • Automation & Monitoring: Implement automated testing, monitoring, and alerting mechanisms to ensure data pipeline reliability and data quality. • Documentation & Best Practices: Maintain comprehensive documentation of data workflows and adhere to best practices in coding, version control, and data governance. Required Qualifications • Experience: 5+ years in data engineering, with hands-on experience using Databricks and Apache Spark. • Programming Skills: Proficiency in Python and SQL; experience with Scala is a plus. • Cloud Platforms: Strong experience with cloud services such as AWS (e.g., S3, Glue, Redshift), Azure (e.g., Data Factory, Synapse), or GCP. • Data Engineering Tools: Familiarity with tools like Airflow, Kafka, and dbt. • Data Modeling: Experience in designing data models for analytics and machine learning applications. • Collaboration: Proven ability to work in cross-functional teams and communicate effectively with non-technical stakeholders. Primary Skill Set • Databricks, Apache Spark, Python, SQL, Scala (optional), ETL/ELT development, Delta Lake. • Cloud platforms (AWS, Azure, GCP), Data modeling. • Cross-functional collaboration, Communication. Secondary Skill Set • Airflow, dbt, Kafka, Hadoop, MLflow, Unity Catalog, Delta Live Tables. • Cluster optimization, Data governance, Security and compliance. • Databricks certifications. •

Requirements