Databricks Full Stack Data Engineer - ETL/Spark

Description :

Our client is seeking an experienced Databricks Full Stack Data Engineer with 56 years of industry experience. The ideal candidate will have a proven track record of working on live projects.

He/she will play a key role in developing, and maintaining scalable data solutions using Databricks and related technologies.

Key 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 Masters 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, data streaming, 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.

- Experience in real-time data processing and streaming technologies (Kafka, Spark Streaming).

- 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 data visualization tool Databricks Genie for data analysis and reporting.

- Knowledge of data governance, security, and compliance best practices.