Senior AWS Data Engineer

Job Description

A leading organization is seeking a Senior AWS Data Engineer to design, build, and support cloud-based data solutions. This is a hands-on individual contributor position focused on developing and maintaining data pipelines, processing data from source systems, ensuring data quality, and delivering curated datasets for downstream consumers.

The role requires strong expertise in AWS technologies, data streaming, ETL and ELT development, and data validation practices. The successful candidate will collaborate with technical stakeholders to support scalable and reliable data engineering solutions.

Responsibilities

• Design, develop, and maintain data pipelines within AWS-based environments.
• Ingest, process, and transform data from source systems into cloud-based storage and data platforms.
• Build and support data streaming solutions using Apache Kafka or similar technologies.
• Develop ETL and ELT processes using AWS-native tools, including AWS Glue.
• Load and manage data within Amazon S3 and relational database platforms such as Amazon RDS and Aurora PostgreSQL.
• Perform data reconciliation, validation, and quality assurance activities to ensure data accuracy and consistency.
• Curate and prepare consumer-ready datasets for analytics and operational use cases.
• Troubleshoot and resolve data pipeline, integration, and performance issues.
• Manage source code repositories and deployment workflows using GitHub and GitHub Actions.
• Support automated deployment and continuous integration and delivery processes.
• Leverage AI-enabled tools to improve engineering productivity and automate data-related workflows.
• Create and maintain technical documentation for data pipelines, transformations, and operational processes.
• Collaborate with cross-functional teams to support data delivery and platform initiatives.

Required Experience and Skills

• 5+ years of data engineering experience.
• Hands-on experience with AWS cloud services.
• Strong experience building and supporting data streaming solutions using Apache Kafka or similar technologies.
• Experience designing and maintaining ETL and ELT processes.
• Experience with AWS Glue.
• Strong knowledge of data reconciliation, data quality, data validation, and data curation practices.
• Experience working with Amazon S3.
• Experience with relational databases, including Amazon RDS and Aurora PostgreSQL.
• Experience managing code repositories with GitHub.
• Experience implementing CI/CD workflows using GitHub Actions.
• Experience utilizing AI tools to improve productivity and automate data engineering processes.
• Strong problem-solving, analytical, and troubleshooting skills.
• Ability to work independently in a hands-on technical environment.

Preferred Experience and Skills

• Experience with MongoDB or other NoSQL database technologies.
• Experience supporting large-scale cloud data platforms.
• Knowledge of modern data integration, transformation, and automation practices.

FAQ

1. What does a Senior AWS Data Engineer do?

A Senior AWS Data Engineer designs, builds, and maintains data platforms and pipelines on Amazon Web Services. The role typically covers data ingestion, transformation, storage, orchestration, performance optimization, and the reliable delivery of data for analytics, reporting, and machine learning.

2. Which AWS services are commonly used by Senior Data Engineers?

Depending on the architecture, engineers may work with services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon EMR, Kinesis, Lambda, Athena, Step Functions, and IAM. The specific combination depends on whether the platform supports batch processing, streaming, data warehousing, or lakehouse workloads.

3. How does a Senior AWS Data Engineer design scalable data pipelines?

Engineers build pipelines that separate ingestion, transformation, storage, and consumption layers while supporting parallel processing and automated orchestration. They also consider fault tolerance, retry handling, partitioning, monitoring, and workload growth when designing production pipelines.

4. What role do Python and SQL play in AWS data engineering?

Python is commonly used for data processing, automation, AWS integrations, and pipeline development, while SQL is used for querying, transformation, validation, and analytical workloads. Strong knowledge of both helps engineers build and troubleshoot production data workflows efficiently.

5. How does AWS data engineering support analytics and machine learning?

The engineer prepares and manages reliable datasets that can be consumed by analysts, data scientists, and machine learning systems. This includes building data pipelines, maintaining analytical storage, supporting feature preparation, and ensuring downstream users can access consistent and timely information.

6. How are data quality and reliability maintained on AWS?

Data quality can be supported through schema validation, automated testing, reconciliation, pipeline monitoring, logging, and alerting. Engineers also establish recovery processes and track pipeline health to identify failures or unexpected data changes before they affect downstream consumers.

7. How does a Senior AWS Data Engineer optimize cloud costs and performance?

Optimization may involve selecting appropriate compute resources, tuning queries, partitioning data, managing storage tiers, scheduling workloads efficiently, and monitoring resource consumption. Senior engineers balance performance requirements with AWS usage costs when making architecture and implementation decisions.