PySpark Developer /Data Engineer

JD:
·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.
Qualifications:
·Bachelor’s or Master’s degree in Computer Science, Engineering, Information
Technology, or related field.
·Minimum of 5 years of experience as a Data Engineer with extensive expertise in AWS,
and PySpark.
·Deep knowledge of SQL and experience with data warehouse design and optimization.
·Strong understanding of AWS services and how they integrate with Databricks and other
data engineering tools.
·Demonstrated ability to design, build, and maintain end-to-end data pipelines.
·Excellent problem-solving abilities, with a track record of implementing complex data
solutions.
Nice to Have
·Experience in managing and automating workflows using Apache Airflow.
·Familiarity with Python, Snowflake, and CI/CD processes using GitHub.
·Strong communication skills for effective collaboration across technical teams and
stakeholders