Job Overview

Amazon is hiring a Data Engineer II for the Compliance Shared Services team within Regulatory Intelligence Safety and Risk (RISC) in Karnataka. This role focuses on building reliable, large-scale data platforms that support compliance programs, reporting, analytics, and data-driven operational decisions.

The engineer will design and maintain data pipelines and infrastructure used to deliver trustworthy information to internal teams. The position combines data engineering, cloud infrastructure, data security, data quality, warehouse and lake architecture, automation, and emerging GenAI capabilities. You will work with technical teams, business stakeholders, analysts, program managers, compliance teams, and legal teams to understand data requirements and turn them into scalable solutions.

This is an experienced data engineering opportunity for professionals who enjoy solving complex data problems, improving pipeline reliability, reducing manual work, and designing systems that can handle compliance-sensitive information at scale.

Key Responsibilities

• Design, build, operate, and improve automated ETL and ELT pipelines using Python, Spark, SQL, and AWS services.
• Develop scalable data warehouse and data lake solutions with attention to table design, partitioning, compression, parallel processing, and overall performance.
• Support reporting and analytics infrastructure used by internal business teams.
• Create data models and transformation processes that produce accurate, consistent, and well-structured datasets.
• Implement data security controls covering encryption, database access, logging, permissions, and appropriate handling of warehouse and lake data.
• Maintain metadata, data catalog information, technical documentation, and user guidance for data assets.
• Work directly with business customers and technical teams to gather requirements and define practical data publishing and consumption patterns.
• Build automation and self-service tools that reduce repetitive engineering work and improve turnaround times for common data requests.
• Explore GenAI-powered tools and agentic workflows that can automate engineering and operational activities.
• Develop data validation pipelines for compliance-sensitive workflows, including matching, verification, referential integrity, and handling of failed records.
• Monitor compute and cluster utilization and improve workload scheduling, reliability, and cost efficiency.
• Evaluate emerging data and AI technologies and determine where they can provide meaningful improvements to the existing data ecosystem.

Required Skills

Candidates should have at least three years of professional data engineering experience and at least four years of SQL experience. Strong practical knowledge of data modeling, data warehousing, and ETL pipeline development is required.

The role calls for engineers who understand how to build reliable data systems rather than simply write individual data transformations. You should be comfortable considering data quality, scalability, security, operational monitoring, and infrastructure cost when designing solutions.

Experience with event-driven orchestration patterns is also important. Familiarity with services and approaches such as EventBridge, Step Functions, and Lambda can help candidates design workflows that respond efficiently to business and engineering events.

Preferred Skills

Experience with AWS data services is highly valued. Relevant technologies include Amazon Redshift, Amazon S3, AWS Glue, Amazon EMR, Amazon Kinesis, Kinesis Data Firehose, AWS Lambda, and IAM roles and permissions.

Candidates may also benefit from experience with non-relational data stores. This can include object storage, document databases, key-value stores, graph databases, and column-family databases.

Another valuable area is GenAI-enabled engineering automation. Experience building or operating agentic workflows using technologies such as AWS Bedrock or MCP-based agents can help candidates contribute to the team's efforts to automate repetitive engineering and operational tasks.

Education

The provided job description does not specify a mandatory degree requirement. Candidates should therefore focus on demonstrating the required professional data engineering, SQL, data modeling, warehousing, pipeline, and cloud experience.

Experience

The minimum requirement is 3+ years of data engineering experience and 4+ years of SQL experience. Applicants should also have practical experience with data modeling, data warehousing, and ETL pipeline development.

Strong candidates will be able to demonstrate ownership of production data pipelines and infrastructure, including monitoring, reliability, performance, security, and operational improvements. Experience supporting analytics or compliance-oriented data workflows is relevant to the nature of this position.

Required Technologies

• Python
• Apache Spark
• SQL
• AWS Redshift
• Amazon S3
• AWS Glue
• AWS Lambda
• EventBridge
• AWS Step Functions
• Data Warehousing
• Data Lakes
• ETL
• ELT
• Data Modeling
• Data Partitioning
• Data Compression
• Parallelization
• Data Encryption
• IAM
• Data Catalog
• Data Validation
• GenAI
• AWS Bedrock
• MCP-based Agents

Preferred technologies also include Amazon EMR, Amazon Kinesis, Kinesis Data Firehose, non-relational databases, object storage, document stores, key-value stores, graph databases, and column-family databases.

Soft Skills

Strong communication and stakeholder collaboration are important because the engineer will work with both technical and business teams. You should be able to understand requirements, explain data architecture choices, document solutions, and guide stakeholders toward appropriate data patterns.

Analytical thinking is essential for identifying data quality problems, pipeline bottlenecks, reliability issues, and infrastructure cost drivers. A proactive approach to automation is also valuable because the team is actively looking for ways to remove repetitive manual work.

Attention to security and data governance is particularly important because the role supports compliance-related data. Candidates should demonstrate accountability when handling sensitive data, designing access controls, validating outputs, and preparing reliable information for downstream users.

Benefits of Working in this Role

This role provides exposure to large-scale data engineering within Amazon's RISC organization and supports systems used by compliance, analytics, program management, and legal teams. The work combines data engineering with cloud infrastructure, security, automation, and emerging AI capabilities.

Amazon states that the team values work-life harmony and offers flexibility as part of its working culture. The organization also highlights knowledge sharing, mentorship, and career development resources. These opportunities can help engineers broaden their technical skills while working on meaningful data challenges.

The position also offers exposure to modern data engineering practices such as event-driven architectures, cost-aware compute, data validation, cloud-native infrastructure, and GenAI-powered engineering automation.

Work Mode

The provided job description does not explicitly state whether the role is onsite, hybrid, or remote.

Location

Karnataka, India. The employer listed for the position is ADCI - Karnataka. The provided job description does not specify a particular city.

Who Should Apply

This opportunity is suitable for experienced Data Engineers, Cloud Data Engineers, ETL Developers, Data Platform Engineers, and professionals with strong SQL and data warehousing backgrounds.

Candidates should consider applying if they have at least three years of data engineering experience, four or more years of SQL experience, and practical experience designing data models and ETL pipelines. Professionals with AWS data engineering experience, event-driven architecture knowledge, data security experience, or GenAI automation exposure may be especially relevant.

Applicants should be comfortable working with large datasets, production pipelines, cloud infrastructure, business stakeholders, and operational requirements. A strong interest in building reliable and scalable data products is important.

Career Growth

This role can help data engineers deepen their expertise across cloud data platforms, distributed processing, data architecture, security, automation, and AI-assisted engineering. Working with compliance and analytics stakeholders can also strengthen business understanding and communication skills.

Engineers can broaden their experience by working across data warehouses, data lakes, event-driven workflows, data validation systems, infrastructure optimization, and GenAI-based automation. These capabilities can support longer-term growth into senior data engineering, data architecture, platform engineering, or technical leadership paths.

Application Advice

Tailor your resume around measurable data engineering work rather than listing technologies without context. Clearly state your years of experience with data engineering and SQL, and describe the types of pipelines, warehouses, lakes, and datasets you have supported.

Highlight experience with Python, Spark, SQL, and AWS services such as Redshift, S3, Glue, Lambda, EMR, Kinesis, or IAM where applicable. Include examples of data modeling, ETL or ELT development, pipeline monitoring, performance optimization, security controls, and cost reduction.

If you have built event-driven workflows using EventBridge, Step Functions, or Lambda, make those projects visible. Candidates with GenAI automation experience should explain what engineering or operational task they automated and how the solution improved efficiency or reliability.

Because this role involves compliance-sensitive information, emphasize experience with data quality, validation, access control, encryption, logging, permissions, documentation, and auditable data processes. Use specific examples wherever possible and only claim technologies you have genuinely used.