Senior Data Platform Engineer

Select Source Solutions; Greenbelt (MD); Remote; USD 120,000 - 170,000; Full time

Job summary

Select Source Solutions is seeking a remote Senior Data Platform Engineer to design and operate scalable cloud-based data platforms for enterprise workloads. You will deliver data ingestion, processing, storage, APIs, and automation components to move data securely and efficiently across the environment.

The role blends data engineering with cloud platform engineering, requiring hands-on production-grade implementation, automation, and collaboration with architects, analytics, and security

Qualifications

• 8+ years of experience in data engineering, software engineering, cloud engineering, or related roles.
• Significant hands-on experience building production data pipelines and data platforms.
• Experience implementing cloud-based data solutions within AWS, Azure, GCP, or comparable environments.
• Strong experience with Python, SQL, or comparable data-engineering technologies.
• Experience automating data and platform workflows.
• Strong understanding of scalable, reliable, production-grade cloud architectures.
• Must be able to hold a security clearance.

Responsibilities

• Design and implement cloud-based data-platform components and services.
• Build scalable data ingestion, transformation, processing, and distribution pipelines.
• Implement cloud storage, data repositories, databases, and supporting platform services.
• Develop APIs and integrations that enable applications and systems to access and exchange data.
• Translate architecture and solution designs into production-ready implementations.
• Build automation that supports data movement, provisioning, processing, monitoring, and operational management.
• Integrate structured, semi-structured, and unstructured data from multiple internal and external sources.
• Develop solutions that improve data reliability, accessibility, quality, scalability, and performance.
• Implement cloud-native services and containerized workloads where appropriate.
• Support batch, event-driven, streaming, and API-based integration patterns.
• Implement monitoring, logging, alerting, and operational controls for data pipelines and platform services.
• Troubleshoot complex data-ingestion, integration, performance, and platform issues.
• Partner with architects, software engineers, analytics teams, security teams, and business stakeholders.
• Support CI/CD, Infrastructure as Code, and automated deployment practices.
• Develop technical documentation, implementation standards, and reusable platform components.

Skills

Data pipelines

Data platforms

Python/SQL

Cloud engineering

Security clearance

Tools

AWS

Azure

GCP

Docker

Kubernetes

Terraform

CloudFormation

Job description

Our client is seeking a remoteSenior Data Platform Engineer to help build and operationalize scalable cloud-based data platforms supporting complex enterprise and technical workloads.

This is a senior hands-on engineering role focused on turning platform and solution architectures into reliable, production-ready capabilities. Working closely with senior architects and application teams, you will implement the data ingestion, processing, storage, integration, API, and automation components that allow data to move securely and efficiently across the environment.

The right person understands both data engineering and cloud platform engineering. You should be comfortable building data pipelines, integrating multiple systems, automating infrastructure and workflows, and solving the operational challenges that come with large-scale data platforms.

What You Will Do

• Design and implement cloud-based data-platform components and services.
• Build scalable data ingestion, transformation, processing, and distribution pipelines.
• Implement cloud storage, data repositories, databases, and supporting platform services.
• Develop APIs and integrations that enable applications and systems to access and exchange data.
• Translate architecture and solution designs into production-ready implementations.
• Build automation that supports data movement, provisioning, processing, monitoring, and operational management.
• Integrate structured, semi-structured, and unstructured data from multiple internal and external sources.
• Develop solutions that improve data reliability, accessibility, quality, scalability, and performance.
• Implement cloud-native services and containerized workloads where appropriate.
• Support batch, event-driven, streaming, and API-based integration patterns.
• Implement monitoring, logging, alerting, and operational controls for data pipelines and platform services.
• Troubleshoot complex data-ingestion, integration, performance, and platform issues.
• Partner with architects, software engineers, analytics teams, security teams, and business stakeholders.
• Support CI/CD, Infrastructure as Code, and automated deployment practices.
• Develop technical documentation, implementation standards, and reusable platform components.

Required Qualifications

• 8+ years of experience in data engineering, software engineering, cloud engineering, or related technical roles.
• Significant hands-on experience building production data pipelines and data platforms.
• Experience implementing cloud-based data solutions within AWS, Azure, GCP, or comparable environments.
• Strong experience with Python, SQL, or comparable data-engineering technologies.
• Experience building data ingestion, transformation, processing, and integration workflows.
• Experience working with cloud object storage, databases, APIs, and data services.
• Experience designing or implementing APIs and system integrations.
• Experience automating data and platform workflows.
• Strong understanding of scalable, reliable, production-grade cloud architectures.
• Experience troubleshooting complex data-platform and integration issues.
• Must be able to hold a secuirty clearance

Preferred Qualifications

• Strong AWS data-platform experience.
• Experience with services such as S3, Glue, Lambda, Step Functions, Redshift, RDS, DynamoDB, Kinesis, MSK, or equivalent technologies.
• Experience with Docker, Kubernetes, and containerized data workloads.
• Experience with Terraform, CloudFormation, or other Infrastructure as Code tools.
• Experience implementing CI/CD pipelines for data or cloud platforms.
• Experience with data lakes, lakehouse architectures, data meshes, or large enterprise data platforms.
• Experience working with scientific, environmental, geospatial, or other complex datasets.
• Experience in federal or regulated technology environments.