Description

You will design and build scalable ETL/ELT pipelines using PySpark to ingest data from databases, logs, APIs, and files.

This role is hybrid.

Responsibilities

• Build reusable, parameterized Spark jobs for batch and micro-batch processing.
• Transform raw transactional and log data into analysis-ready datasets within the Data Hub and analytical data marts.
• Optimize PySpark job performance and scalability across large data volumes.
• Ensure data quality, consistency, and lineage across all ingestion flows.
• Collaborate with Data Architects and Data Scientists to implement ingestion logic.

Required Skills

• 4+ years of experience in data engineering with a focus on PySpark and Spark.
• Proficiency in Python and data processing libraries.
• Expertise in building pipelines from relational, semi-structured (JSON, XML), and unstructured sources.
• Strong SQL skills for querying and validating data in Amazon Redshift or PostgreSQL.
• Experience with distributed computing frameworks like Spark on EMR or Databricks.
• Knowledge of data lake and data warehouse architectures.
• Experience with workflow orchestration tools such as AWS Step Functions.
• Ability to work with AWS S3, Glue, EMR, and Redshift.

Preferred Skills

• Experience with Spark Structured Streaming or Kafka.
• Familiarity with Delta Lake for large-scale data storage.
• Knowledge of DevOps/CI-CD pipelines using Git or Jenkins.

Key Skills

Education

Any Gradute