Senior Data Engineer (AWS & Snowflake)

Description

• Data Pipeline Development: Build and maintain efficient, scalable, and reliable pipelines to support analytics and reporting workloads.
• Data Integration: Implement seamless integration across diverse data sources (structured, semi-structured, and unstructured) into Snowflake and AWS-based data platforms (Postgres/Aurora Postgres/Dynamo DB)
• Data Governance: Establish and enforce data governance frameworks including metadata management, data lineage, data quality and data entitlement.
• Cloud Engineering: Leverage AWS services (e.g., S3, Glue, Lambda, Redshift, EMR) to design cloud-native data solutions.
• Performance Optimization: Monitor, troubleshoot and optimize data workflows for speed, scalability, and cost efficiency.
• Collaboration: Partner with data architects, analysts and business stakeholders to translate requirements into technical solutions.
• Best Practices: Drive adoption of engineering best practices, including CI/CD, automation, and Infrastructure-as-Code for data platforms.
• Leveraging AI: Any experience in leveraging AI in execution or implementation of data pipelines and data integration solutions.

Requirements

• 10-12 years of hands-on experience in data engineering.
• Strong hands-on expertise in Snowflake data modelling, performance tuning, CDC, security and governance (Snowpipe, Dynamic Tables, DBT, Streams, RBAC).
• Hands-on experience with AWS services (S3, Glue, Postgres, Redshift, EMR, IAM).
• Proficiency in RDBMS concepts, SQL and programming languages such as Python or Scala.
• Experience with ETL/ELT frameworks and workflow orchestration tools ( Snaplogic, Airflow, DBT).
• Good understanding of data governance principles and implementation experience
• Familiarity with DevOps practices and CI/CD pipelines for data engineering.
• Excellent problem-solving, communication and stakeholder management skills.

Preferred Qualifications

• Exposure to big data technologies (Spark, Hadoop).
• Experience with real-time data streaming (Kafka, Kinesis).
• Knowledge of data cataloging tools (Collibra, Alation).
• Experience of using AI tools (Cloude, Cortex)
• Prior experience in implementing enterprise-scale data modernization initiatives.