Description

You will architect and implement scalable data hub solutions on AWS.

Responsibilities

• Architect and implement scalable data hub solutions on AWS using best practices for ingestion, transformation, storage, and access control.
• Design and build data pipelines to extract, transform, and load data from databases, APIs, and flat files using AWS Glue, AWS Batch, or custom ETL processes.
• Define data models, lineage, and quality standards, including CDM, LDM, and PDM.
• Select AWS services such as S3, Glue, Redshift, Athena, and Lambda based on volume, access patterns, and performance.
• Implement data cleansing and normalization techniques to ensure data quality and manage ingestion schedules and error handling.

Required Skills

• 5+ years of experience in data architecture and engineering.
• Deep expertise in AWS services: S3, Glue, Redshift, Athena, Lake Formation, Lambda, CloudWatch, and EventBridge.
• Proficiency in Python, SQL, and PySpark for data processing and manipulation.
• Strong experience in ETL/ELT processes, data cleansing, and data transformation.
• Expertise in dimensional and snowflake data modeling for data warehousing and data lakes.
• Knowledge of GCP Data Architecture and Data Lakehouse Architecture.
• Experience with MDM, Conceptual Data Architecture, and Dimensional Data Modeling.
• Understanding of data governance, including data classification, access control, and lineage tracking.

Preferred Skills

• Experience with Informatica IICS and ICDQ.

Key Skills

Education

Any Graduate