Data Engineer II MIDAS Digital Acceleration

Amazon; Chennai District; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Amazon Digital Acceleration seeks an analytical data engineer to design and deliver foundational datasets spanning orders, subscriptions, discovery, promotions and royalties. You will build scalable data pipelines on AWS and partner with cross-functional teams to enable faster product decisions.

An ideal candidate has strong ETL, data modeling and big data skills, plus solid business judgment and communication abilities. You will join the MIDAS team supporting Amazon's digital analytics efforts.

Qualifications

• 3+ years of data engineering experience.
• 4+ years of SQL experience.
• Experience with data modeling, warehousing and building ETL pipelines.

Responsibilities

• Develop data products, infrastructure and data pipelines leveraging AWS services and internal tools.
• Improve solutions to enhance scale, quality, efficiency, data availability and compliance.
• Partner with Developers, BI Engineers, MLEs, Scientists, and PMs to build scalable data pipelines.
• Drive operational excellence and build automation to reduce manual operations.

Skills

Data engineering

SQL

ETL

Data modeling

Big data

Tools

AWS Redshift

AWS S3

AWS Glue

AWS EMR

AWS Kinesis

AWS Lambda

IAM

Job description

Job Description:

Are you excited about the digital media revolution and passionate about designing and delivering advanced analytics that directly influence the product decisions of Amazons digital businesses. Do you see yourself as a champion of innovating on behalf of the customer by turning data insights into action?

The Amazon Digital Acceleration (DA) org is looking for an analytical and technically skilled data engineer to join our team. In this role, you will play a critical part in developing foundational analytical datasets spanning orders, subscriptions, discovery, promotions, pricing and royalties. Our mission is to enable digital clients to easily innovate with data on behalf of customers and make product and customer decisions faster.

An ideal individual is someone who has deep data engineering skills around ETL, data modeling, database architecture and big data solutions. This individual should have strong business judgement, excellent written and verbal communication skills.

Key job responsibilities

• 1. Develop data products, infrastructure and data pipelines leveraging AWS services (such as Redshift, Kinesis, EMR, Lambda etc.) and internal BDT tools (Datanet, Cradle, QuickSight etc.
• 2. Improve existing solutions/build solutions to improve scale, quality, IMR efficiency, data availability, consistency & compliance.
• 3. Partner with Software Developers, Business Intelligence Engineers, MLEs, Scientists, and Product Managers to develop scalable and maintainable data pipelines on both structured and unstructured (text based) data.
• 4. Drive operational excellence strongly within the team and build automation and mechanisms to reduce operations

About the team

The MIDAS team operates within Amazons Digital Analytics (DA) engineering organization, building analytics and data engineering solutions that support cross-digital teams. Our platform delivers a wide range of capabilities, including metadata discovery, data lineage, customer segmentation, compliance automation, AI-driven data access through generative AI and LLMs, and advanced data quality monitoring. Today, more than 100 Amazon business and technology teams rely on MIDAS, with over 20,000 monthly active users leveraging our mission-critical tools to drive data-driven decisions at Amazon scale.

Basic Qualifications

• - 3+ years of data engineering experience
• - 4+ years of SQL experience
• - Experience with data modeling, warehousing and building ETL pipelines

Preferred Qualifications

• - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
• - Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

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