Senior Data Engineer – AWS & Snowflake

We are looking for a skilled Senior Data Engineer with strong AWS and Snowflake expertise to design, develop, and optimize scalable cloud-based data pipelines and modern data warehousing solutions. The role involves hands-on development, building high-performance ELT/ETL pipelines, and contributing to the architecture of a scalable cloud data platform. The ideal candidate has deep expertise in Snowflake data warehousing, AWS data services, and Python-based data engineering, along with a strong understanding of modern data engineering best practices.

Responsibilities

Develop and maintain scalable batch and real-time data pipelines on AWS; Build and optimize ELT/ETL pipelines using Snowflake, AWS Glue, and Spark on EMR; Design and implement Snowflake data warehouse solutions, including schema design, clustering, and performance optimization; Develop and manage Snowflake data models (Star/Snowflake schemas) for analytics and reporting; Implement workflow orchestration using Apache Airflow (MWAA); Support serverless data processing using AWS Lambda; Design and manage data lake architectures using Amazon S3 and Snowflake; Implement data ingestion frameworks for structured and semi-structured data; Optimize Snowflake query performance, storage usage, and compute costs; Apply data governance, security, and role-based access controls using AWS DataZone and Snowflake features; Monitor and troubleshoot data pipelines and platform issues using Amazon CloudWatch and other monitoring tools; Collaborate with Analytics, BI, Data Science, and Business teams to enable data-driven decision-making; Write clean, scalable, and efficient code, and participate in peer code reviews and architecture discussions; Ensure data quality, reliability, and compliance with governance standards

Requirements

6–10+ years of experience in Data Engineering or related roles; 3+ years of hands-on experience designing and building data platforms on AWS; Strong experience with Snowflake data warehouse architecture, development, and optimization; Proficiency with AWS data services, including: AWS Glue, Amazon EMR (Spark), AWS Lambda, Apache Airflow (MWAA), Amazon EC2, Amazon CloudWatch, Amazon Redshift, Amazon DynamoDB, AWS DataZone, Amazon S3; Strong programming skills in Python for data engineering and pipeline development; Advanced SQL skills with query optimization and performance tuning; Strong understanding of data warehousing concepts and dimensional modeling (Star/Snowflake); Experience building scalable ETL/ELT pipelines and handling large-scale datasets; Familiarity with Agile/Scrum delivery environments; Strong problem-solving and analytical skills

Nice to have

AWS Professional Certification (Solutions Architect / Data Analytics Specialty); Snowflake Certification (SnowPro Core or Advanced); Experience with real-time streaming technologies (Kinesis, Kafka); Exposure to data governance, data catalog, and metadata management tools; Experience with CI/CD pipelines for data engineering workflows; Knowledge of cost optimization strategies in AWS and Snowflake