Responsibilities

Translate business requirements into scalable data engineering solutions through conceptual analysis, problem-solving, and data analysis.
Own data engineering initiatives and collaborate with subject matter experts, business analysts, data analysts, and other stakeholders.
Identify suitable data sources, mapping strategies, and business models for enterprise data integration.
Build and maintain ingestion pipelines for databases, delimited files, and other source systems.
Perform complex transformations and load curated data into Amazon S3.
Develop AWS Glue mappings, maintain the Glue Data Catalog, and implement dependable ETL workflows.
Design data warehouse solutions using Python, PySpark, Amazon Redshift, and related AWS services.
Maintain data quality, reliability, consistency, cleanliness, standards, and governance across multiple sources.
Support Change Data Capture and event-streaming use cases, and evaluate Parquet, Apache Iceberg, and unstructured data technologies.

Requirements

4–6 years of overall experience in data engineering, including 2–3 years of hands-on experience in data ingestion, ETL, and data warehousing.
Strong practical knowledge of AWS cloud services for data management and governance.
Proficiency in Python and PySpark for data processing and transformation.
Hands-on experience with AWS Glue, Glue Data Catalog, Amazon S3, and Amazon Redshift.
Experience designing scalable data pipelines and working with ETL, data integration, and data warehouse technologies.
Exposure to CDC, event streaming, Parquet, Apache Iceberg, and unstructured data processing.
Strong analytical, communication, collaboration, and problem-solving abilities.
Delhi NCR local candidates only. Previous MNC experience is required. Immediate joiners or candidates serving a notice period of no more than 15 days are preferred.
The selection process includes two virtual interview rounds.