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Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and optimize scalable PySpark data pipelines on Cloudera Data Platform with integration of Apache Iceberg and Airflow for workflow orchestration.
Implement ETL/ELT processes and build cloud-native data solutions leveraging AWS services such as S3, Glue, Lambda, EMR, and others to support analytics, reporting, and operational workloads.
Own end-to-end solution delivery including requirements analysis, solution design, development, testing, deployment, production support, and establishing best practices and CI/CD pipelines.
Minimum Requirements
Bachelor's degree in Computer Science, Information Systems, Engineering, or related field.
10+ years of IT experience with at least 6 years in Data Engineering and Big Data development.
Strong hands-on experience with PySpark, Cloudera Data Platform (CDP), Apache Iceberg table architecture, and Apache Airflow workflow orchestration.
Experience with AWS services relevant to data engineering (e.g., S3, Glue, Lambda, EMR) and familiarity with Git, CI/CD pipelines, and DevOps practices.
Ideal Candidate Profile
Senior-level professional with extensive experience (10-12+ years) in enterprise-scale cloud migration and modernization programs focused on data engineering.
Hands-on developer skilled in distributed data processing, performance tuning, data quality management, and cloud-based architectures.
Experienced in collaborating with cross-functional teams (data architects, cloud engineers, business analysts) to design and deliver high-performance, scalable data platforms using modern technologies like PySpark, Iceberg, Airflow, and AWS.