Databricks Full Stack Data Engineer

Role

Design, Build & Optimize Scalable Data Solutions and End-to-End Pipelines

Responsibilities

• Develop, and deploy end-to-end data pipelines and solutions on Databricks, integrating with various data sources and systems.
• Collaborate with cross-functional teams to understand data, and deliver effective BI solutions.
• Implement data ingestion, transformation, and processing workflows using Spark (PySpark/Scala), SQL, and Databricks notebooks.
• Develop and maintain data models, ETL/ELT processes ensuring high performance, reliability, scalability and data quality.
• Build and maintain APIs and data services to support analytics, reporting, and application integration.
• Ensure data quality, integrity, and security across all stages of the data lifecycle.
• Monitor, troubleshoot, and optimize pipeline performance in a cloud-based environment.
• Write clean, modular, and well-documented Python/Scala/SQL/PySpark code.
• Integrate data from various sources, including APIs, relational and non-relational databases, IoT devices, and external data providers.
• Ensure adherence to data governance, security, and compliance policies.

Required Skills and Experience:

• Bachelors or Master’s degree in Computer Science, Engineering, or a related field.
• 5-6 years of hands-on experience in data engineering, with a strong focus on Databricks and Apache Spark.
• Strong programming skills in

  Python/PySpark

  and/or

  Scala

  , with a deep understanding of

  Apache Spark.

• Experience with Azure Databricks.
• Strong SQL skills for data manipulation, analysis, and performance tuning.
• Strong understanding of data structures and algorithms, with the ability to apply them to optimize code and implement efficient solutions.
• Strong understanding of data architecture, data modeling, ETL/ELT processes, and data warehousing concepts.
• Experience building and maintaining ETL/ELT pipelines in production environments.
• Familiarity with Delta Lake, Unity Catalog, or similar technologies.
• Experience working with structured and unstructured data, including JSON, Parquet, Avro, and time-series data.
• Familiarity with CI/CD pipelines and tools like Azure DevOps, version control (Git), and DevOps practices for data engineering.
• Excellent problem-solving skills, attention to detail, and ability to work independently or as part of a team.
• Strong communication skills to interact with technical and non-technical stakeholders.

Preferred Qualifications:

• Experience with Delta Lake and Databricks Workflows.
• Exposure to real-time data processing and streaming technologies (Kafka, Spark Streaming).
• Exposure to data visualization tool Databricks Genie for data analysis and reporting.
• Knowledge of data governance, security, and compliance best practices.