PySpark Developer

Key Responsibilities:
Design and build robust, scalable ETL/ELT pipelines using PySpark to ingest data from diverse sources (databases, logs, APIs, files).
Transform and curate raw transactional and log data into analysis-ready datasets in the Data Hub and analytical data marts.
Develop reusable and parameterized Spark jobs for batch and micro-batch processing.
Optimize performance and scalability of PySpark jobs across large data volumes.
Ensure data quality, consistency, lineage, and proper documentation across ingestion flows.
Collaborate with Data Architects, Modelers, and Data Scientists to implement ingestion logic aligned with business needs.
Work with cloud-based data platforms (e.g., AWS S3, Glue, EMR, Redshift) for data movement and storage.
Support version control, CI/CD, and infrastructure-as-code where applicable.
Participate in Agile ceremonies and contribute to sprint planning, story grooming, and demos.

Required Qualifications:
4+ years of experience in data engineering, with strong focus on PySpark/Spark for big data processing.
Expertise in building data pipelines and ingestion frameworks from relational, semi-structured (JSON, XML), and unstructured sources (logs, PDFs).
Proficiency in Python with strong knowledge of data processing libraries.
Strong SQL skills for querying and validating data in platforms like Amazon Redshift, PostgreSQL, or similar.
Experience with distributed computing frameworks (e.g., Spark on EMR, Databricks).
Familiarity with workflow orchestration tools (e.g., AWS Step Functions, or similar).
Solid understanding of data lake / data warehouse architectures and data modeling basics.

Preferred Qualifications:
Experience with AWS data services: Glue, S3, Redshift, Lambda, CloudWatch, etc.
Familiarity with Delta Lake or similar for large-scale data storage.
Exposure to real-time streaming frameworks (e.g., Spark Structured Streaming, Kafka).
Knowledge of data governance, lineage, and cataloging tools (e.g., AWS Glue Catalog, Apache Atlas).
Understanding of DevOps/CI-CD pipelines for data projects using Git, Jenkins, or similar tools