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Data Engineer II

Amazon

Data Engineer II

Amazon

Bengaluru, KA, IND

·

On-site

·

Full-time

·

5d ago

FBA is seeking a Data Engineer to build AI-native data infrastructure that embeds intelligence as a foundational feature. If you enjoy innovating and want to contribute to an industry-changing business while building next-generation intelligent data platforms, this role is for you.

Core Responsibilities:

  • AI-Native Infrastructure & Real-Time Processing

  • Engineer AI-native infrastructure supporting real-time data processing for AI/ML inference, training, and continuous learning

  • Build semantic layers and knowledge graphs enabling intelligent query routing and context-aware data access

  • Develop infrastructure for agentic AI systems with multi-agent orchestration

  • Implement GenAI-powered data quality, entity resolution, and metadata management

  • Data-as-a-Product Delivery

  • Own end-to-end accountability for data products from ingestion to consumption

  • Deliver data products with clear SLAs, quality metrics, and customer satisfaction measures

  • Build self-service platforms with embedded governance, lineage, and discovery

  • Establish data contracts and APIs for reliable, versioned data consumption

  • AWS Infrastructure & Pipeline Engineering

  • Manage AWS resources: EC2, Lambda, S3, Redshift, Kinesis, EMR, Sage Maker, Bedrock, Neptune

  • Build high-quality pipelines supporting analysts, data scientists, and AI agents

  • Implement CDC and event-driven architectures for real-time data availability

  • Deploy infrastructure-as-code using CDK/Terraform

  • Required Qualifications

  • 5+ years in data engineering with cloud-native architectures

  • Strong AWS expertise (Redshift, S3, Glue, Kinesis, EMR)

  • Proven experience with real-time streaming (Kafka, Kinesis, Flink)

  • Hands-on AI/ML infrastructure experience (Sage Maker, Bedrock)

  • Proficiency in Python, SQL, and infrastructure-as-code

  • Preferred Qualifications

  • Knowledge graphs (Neptune, Neo4j) and semantic layers

  • GenAI applications and LLM integration patterns

  • Vector databases and feature stores

  • Data mesh and domain-oriented architecture

Build the foundational infrastructure powering next-generation AI-enabled data products at Amazon FBA.

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, Fire Hose, 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)

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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About Amazon

Amazon

Amazon

Public

Amazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.

10,001+

Employees

Seattle

Headquarters

Reviews

2.9

10 reviews

Work Life Balance

2.8

Compensation

3.7

Culture

2.5

Career

2.3

Management

2.1

35%

Recommend to a Friend

Pros

Good pay and compensation

Strong benefits package

Flexible scheduling options

Cons

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

Salary Ranges

2 data points

L2

L3

L4

L5

L6

L2 · Data Analyst L2

0 reports

$108,330

total / year

Base

$43,332

Stock

$54,165

Bonus

$10,833

$75,831

$140,829

Interview Experience

10 interviews

Difficulty

3.7

/ 5

Duration

21-35 weeks

Offer Rate

20%

Experience

Positive 10%

Neutral 10%

Negative 80%

Interview Process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge