Sr Data Engineer with Python & Databricks

This role focuses on building efficient data pipelines, implementing best practices in data engineering, and ensuring system reliability and performance. The Data Engineer works closely with data scientists, analysts, and software engineers to deliver optimized data solutions for both batch and real-time processing.

Key Responsibilities

• Design, develop, and maintain end-to-end data pipelines and ETL/ELT workflows using Python, SQL, and modern orchestration tools (e.g., Airflow, dbt).
• Architect and optimize data storage solutions, including data lakes and data warehouses, using Databricks, Delta Lake, and cloud-native services.
• Build scalable data processing solutions leveraging Databricks notebooks, jobs, and clusters for both batch and streaming data workloads.
• Develop and manage Databricks workflows using Spark (PySpark, SQL, or Scala) to transform, cleanse, and aggregate large datasets.
• Design, develop, and implement complex data integrations across Databricks, cloud platforms (AWS, Azure, or GCP), enterprise applications, APIs, and modern data ecosystems to enable reliable, scalable data exchange and processing.
• Implement data quality checks, schema validation, and monitoring to ensure data accuracy and reliability.
• Optimize Databricks cluster configurations and job performance to minimize cost and maximize throughput.
• Collaborate with DevOps teams to automate deployments, CI/CD pipelines, and infrastructure-as-code (IaC) for data systems.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related technical field.
• Advanced proficiency in Python and SQL for data manipulation, transformation, and automation.
• Deep experience with Databricks, including Spark optimization, Delta Lake management, job orchestration, and workspace administration.
• Strong understanding of distributed data processing, partitioning strategies, and performance tuning in Databricks and Spark.
• Demonstrated experience designing and implementing enterprise-scale data integrations across Databricks, cloud platforms (AWS, Azure, or GCP), enterprise applications, APIs, and modern data ecosystems.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP) and their data ecosystems (e.g., S3, ADLS, BigQuery, Snowflake).