Pyspark - Data Architect

Pyspark JD:

Responsibilities

1. Data Pipeline Development: Design, develop and maintain highly scalable and optimized ETL pipelines using PySpark on the Cloudera Data Platform ensuring data integrity and accuracy.
2. Data Ingestion: Implement and manage data ingestion processes from a variety of sources (relational databases, APIs, file systems) to the data lake or data warehouse on CDP.
3. Data Transformation and Processing: Use PySpark to process, cleanse and transform large datasets into meaningful formats that support analytical needs and business requirements.
4. Performance Optimization: Conduct performance tuning of PySpark code and Cloudera components, optimizing resource utilization and reducing runtime of ETL processes.
5. Data Quality and Validation: Implement data quality checks, monitoring and validation routines to ensure data accuracy and reliability throughout the pipeline.
6. Automation and Orchestration: Automate data workflows using tools like Apache Oozie, Airflow, or similar orchestration tools within the Cloudera ecosystem.
7. Monitoring and Maintenance: Monitor pipeline performance, troubleshoot issues, and perform routine maintenance on the Cloudera Data Platform and associated data processes.
8. Collaboration: Work closely with other data engineers, analysts, product managers, and other stakeholders to understand data requirements and support various data-driven initiatives.
9. Documentation: Maintain thorough documentation of data engineering processes, code, and pipeline configurations.

Qualifications

1. Education and Experience: Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field.
2. 3 years of experience as a Data Engineer with a strong focus on PySpark and the Cloudera Data Platform.

1. Technical Skills:
2. PySpark: Advanced proficiency in PySpark including working with RDDs, DataFrames, and optimization techniques.
3. Cloudera Data Platform: Strong experience with Cloudera Data Platform (CDP) components including Cloudera Manager, Hive, Impala, HDFS, and HBase.
4. Data Warehousing: Knowledge of data warehousing concepts, ETL best practices, and experience with SQL-based tools (Hive, Impala).
5. Big Data Technologies: Familiarity with Hadoop, Kafka, and other distributed computing tools.
6. Orchestration and Scheduling: Experience with Apache Oozie, Airflow, or similar orchestration frameworks.
7. Scripting and Automation: Strong scripting skills in Linux.

1. Soft Skills:
2. Strong analytical and problem-solving skills.
3. Excellent verbal and written communication abilities.
4. Ability to work independently and collaboratively in a team environment.
5. Attention to detail and commitment to data quality.