AWS Data Engineer - Redshift & Snowflake

About TechMango:

Founded in 2014, Techmango Technology Services Private Limited is a trusted global technology partner with a presence in India, the USA, and the UAE. The company specializes in delivering transformative digital solutions by integrating Generative AI, Data Analytics, and Intelligent Automation with advanced engineering expertise. Techmango provides services in SaaS product development, enterprise applications, cloud & DevOps, mobile solutions, and app modernization to create scalable platforms with tangible business outcomes. Recognized as the Best Data Engineering Service Provider and a NASSCOM partner, the CMMi Level 3 company is known for quality, security, and innovation in data-driven technologies.

Job Title: AWS Data Engineer - Redshift & Snowflake

Location: Madurai - Remote (For Onboarding & Monthly Once need to visit base Location Madurai)

Experience: 8+ Years

Employment Type: Full-time

Notice Period: Prefers Immediate Joiner Only or Serving Notice and who can join on or before 31st Dec'25

About the Role

We are looking for a skilled AWS Data Engineer with hands-on experience in building and optimizing data pipelines, designing data architectures, and working on cloud-native data solutions. The ideal candidate should have strong technical knowledge in AWS services, data integration, and real-time/batch data processing.

Key Responsibilities:

Were looking for a seasoned AWS Data Engineer to join our Engineering organization and take ownership of the systems that power our data-driven decisions. Youll architect and maintain high-performance data pipelines and databases across platforms like MSSQL, Redshift, and Snowflakeensuring data is reliable, accessible, and optimized for scale. If you thrive on solving complex problems, love optimizing systems for performance, and want to be the go-to expert in a collaborative, high-impact environment, wed love to meet you.

What You Will Do:

Design and maintain scalable, efficient, and well-partitioned schemas in MSSQL, Redshift, and Snowflake

Architect and optimize complex queries, stored procedures, indexing strategies, and partitioning for large datasets

Build, monitor, and maintain data pipelines that ensure timely and accurate delivery of data to internal and external consumers

Own and enforce data refresh SLAs, ensuring availability, consistency, and reliability across production and reporting environments

Collaborate with software engineers, analysts, and DevOps teams to ensure data models and queries align with product and reporting requirements Proactively identify and remediate performance bottlenecks, slow queries, and data inconsistencies

Implement and manage database change workflows using schema migration/versioning tools

Define and promote best practices for data access, security, compliance, and observability

What You Will Bring:

8+ years of experience in database engineering or backend systems development

Deep expertise with MySQL, Amazon Redshift, and Snowflake, including schema design and performance optimization

Design, build, and maintain scalable data pipelines and ETL processes tailored to client needs.

Proven track record maintaining data freshness SLAs and data quality across production pipelines

Hands-on experience with T-SQL, LinkSQL, query optimization, and indexing strategies

Experience with query optimization, mapping tables to blocks and partitions, sub-table structure and keying/indexing for efficiency

Experience of relational data modeling and schema versioning in support of software development.

Experience as the sole or lead database expert on a development team.

Familiarity with source control systems (e.g., Git/Bitbucket) and CI/CD integration.

Strong problem solving skills are desirable.

Transforming product requirements into workable solutions, in collaboration with several development and testing teams.

Writing robust functions, procedures and scripts using SQL.

Being heavily involved in the day to day running of the business dealing with support and performance issues.

Diagnose and troubleshoot data-related issues, providing quick and effective resolutions.

Analyze data workflows and identify areas for improvement, recommending and implementing solutions to optimize performance.

Utilize a proactive approach to foresee potential problems and address them before they impact operations.