Develop and optimize data processing jobs using PySpark to handle complex data transformations and aggregations efficiently.
Design and implement robust data pipelines on the AWS platform, ensuring scalability and efficiency (Databricks exposure will be an advantage)
Leverage AWS services such as EC2, S3, etc. for comprehensive data processing and storage solutions.
Expertly manage SQL database schema design, query optimization, and performance tuning to support data transformation and loading processes.
Design and maintain scalable and performant data warehouses, employing best practices in data modeling and ETL processes.
Utilize modern data platforms for collaborative data science, integrating seamlessly with various data sources and types.
Ensure high data quality and accessibility by maintaining optimal performance of Databricks clusters and Spark jobs.
Develop and implement security measures, backup procedures, and disaster recovery plans using AWS best practices.
Manage source code and automate deployment using GitHub along with CI/CD practices tailored for data operations in cloud environments.
Provide expertise in troubleshooting and optimizing PySpark scripts, Databricks notebooks, SQL queries, and Airflow DAGs.
Keep abreast of latest developments in cloud data technologies and advocate for the adoption of new tools and practices that can benefit the team.
Use Apache Airflow to orchestrate and automate data workflows, ensuring timely and reliable execution of data jobs across various data sources and systems.
Collaborate closely with data scientists and business analysts to design data models and pipelines that support advanced analytics and machine learning projects.

Databricks Pyspark

Job Description

Required Skills