AWS Data Senior Technical Specialist - Glue, Snowflake and DevOps

Job Summary

Data Engineer

Role Description

Data Engineers are responsible for designing, building, and operating scalable data pipelines to source data from systems of record and land it into AWS Data Lake and Snowflake platforms using approved architectural patterns. They enable reliable ingestion, transformation, and availability of data for downstream analytics and reporting.

Key Responsibilities

1. Develop and implement amazon glue workflows for data extraction, transformation, and loading (etl).
2. Design and optimize sql queries for efficient data retrieval and manipulation.
3. Utilize python programming for automation and data processing tasks.
4. Implement devops solutions to streamline development and deployment processes.
5. Manage and optimize snowflake data warehouse for scalability and performance.

Skill Requirements

Key Responsibilities

• Design and implement data ingestion pipelines from source systems into AWS Data Lake and Snowflake

• Develop and manage ETL/ELT pipelines, including extraction, transformation, and load processes

• Build and manage automated data loads, scheduling, monitoring, and error handling mechanisms

• Apply approved architectural patterns (e.g., Wherescape, AWS Lambda using Python) for ingestion and processing

• Perform data profiling and support identification of data quality issues, gaps, and inconsistencies

• Partner with Data Modelers to ensure ingestion supports downstream modeling and analytics use cases

• Support testing, defect resolution, deployment, and operational stabilization activities

• Ensure compliance with architectural standards, security policies, and governance frameworks

Other Requirements

Required Experience

• 6+ years of enterprise data engineering experience

• Strong hands-on experience with Snowflake and cloud data platforms (AWS preferred)

• Proficiency in Python and SQL for data processing and pipeline development

• Experience designing and implementing ETL/ELT frameworks

• Familiarity with data lake architectures and ingestion frameworks

• Experience with data quality validation, monitoring, and operationalization

Understanding of enterprise data governance and security practices