Data Engineer

We are seeking a highly skilled, future-ready Data Engineer to help build the data platform that powers trusted Business Intelligence today and AI-enabled experiences tomorrow. This role is grounded in strong data engineering fundamentals - scalable pipelines, clean data modeling, reliable integrations, database performance, governance, and data quality - while also advancing the organization's ability to support front-end data products, MCPs, and AI-agent workflows. 

The ideal candidate brings deep Python capability, disciplined GitHub-based development, deployment awareness, excellent technical documentation habits, and strong ownership of production-quality data solutions. SS&E is a tech-forward organization that believes in the responsible use of AI, with responsibility and accountability remaining with the individual. This position encourages the responsible use of AI-assisted engineering tools such as OpenAI Codex, Claude Code, or comparable tools under SS&E's enterprise agreements to support development, testing, refactoring, debugging, documentation, and codebase understanding; however, these tools should not be relied upon as a substitute for sound data engineering judgment, clean architecture, secure coding practices, or hands-on technical ownership. 

Who You Are: 

Minimum Qualifications  

• Bachelor's or Master's Degree in Computer Science, Engineering, Data Analytics, Information Systems, or a related field.

• 1-2 years of experience as a Data Engineer, Analytics Engineer, Software Engineer with data focus, or similar technical role building data pipelines, models, and data platforms.

• Strong foundation in data engineering fundamentals, including ETL/ELT pipelines, data modeling, relational databases, API integrations, data validation, orchestration concepts, and scalable data architecture.

• Expertise in Python, including experience building production-ready scripts, data pipelines, automation workflows, APIs, backend services, or data processing frameworks.

• Proficiency in SQL, including SQL-based data modeling, query optimization, transformation logic, and troubleshooting.

• Hands-on experience with SQL Server, Azure SQL Managed Instance, Snowflake, or other relational database systems.

• Experience extracting, transforming, and integrating data from complex APIs, SaaS platforms, operational systems, and external data sources.

• Experience using GitHub for version control, pull requests, code review, branching strategies, release documentation, and collaborative development workflows.

• Experience creating and maintaining clear technical documentation for data pipelines, data models, APIs, deployment steps, system dependencies, support procedures, and runbooks.

• Knowledge of how data infrastructure supports BI tools, React-based front ends, internal applications, and AI-enabled workflows.

• Familiarity with Azure or AWS cloud services used for data storage, compute, integration, deployment, and monitoring, with preference for Azure-based data environments.

• Experience developing governed datasets, curated reporting layers, or datasets for Business Intelligence, visualization, and operational analytics use cases.

• Strong analytical, troubleshooting, and problem-solving skills with the ability to improve existing systems without disrupting business operations.

• Effective communication skills and ability to collaborate with technical and non-technical stakeholders.

Preferred Qualifications:  

• Experience designing AI-ready data infrastructure, MCPs, governed context layers, or data services that support internal AI agents and automation.

• Experience using OpenAI Codex, Claude Code, or comparable AI-assisted engineering tools in GitHub-connected, CLI, IDE, or deployment-adjacent workflows under appropriate enterprise usage standards.

• Experience with NoSQL databases.

• Experience writing developer-friendly documentation, data dictionaries, architecture notes, and operational support guides.

What You’ll Do:

• Design, build, and optimize scalable data pipelines using strong data engineering fundamentals, including ETL/ELT design, data modeling, validation, monitoring, and performance-aware architecture.

• Develop production-ready Python scripts, services, automation workflows, and data processing frameworks that are reliable, testable, maintainable, and well documented.

• Use GitHub as a core engineering workflow, including version control, branching, pull requests, code reviews, release notes, deployment readiness, and collaborative development standards.

• Create and maintain clear technical documentation for pipelines, APIs, MCPs, database objects, data models, deployment steps, dependencies, support procedures, and operational runbooks.

• Where appropriate, use approved AI-assisted development workflows responsibly to support coding, debugging, testing, refactoring, documentation, and codebase understanding while maintaining human review, secure development practices, and sound engineering judgment.

• Integrate data from internal systems, third-party platforms, APIs, and external partners while ensuring consistency, accessibility, data quality, and fit-for-purpose structures.

• Administer and optimize relational databases and cloud data services, including Azure SQL Managed Instance, SQL Server, Snowflake or similar platforms, with responsibility for schema design, tuning, troubleshooting, and lifecycle management.

• Develop governed data warehouse, data mart, and semantic-layer assets that support trusted reporting, operational analytics, executive decision-making, and scalable Business Intelligence delivery.

• Build AI-ready data infrastructure, including MCP patterns, governed access points, reusable data services, and structured context layers that allow internal tools and agents to interact with enterprise data safely and effectively.

• Prepare well-modeled datasets for  Power BI, D3, React-based front ends, and other application or visualization experiences so users can access clear, accurate, and actionable insights.

• Partner with Business Intelligence, Ticketing, Information Technology, Partnerships, Premium Services, Finance, and other stakeholders to translate business needs into durable technical solutions.

• Continuously improve integration, transformation, deployment, documentation, and data preparation processes so SS&E can deliver faster reporting, stronger automation, and future-ready digital experiences.

Physical Requirements 

• Ability to work at a computer for extended periods of time.
• Ability to participate in meetings, presentations, and collaborative work sessions.
• Ability to work in an office environment with occasional on-site collaboration.
• Ability to travel locally for meetings or organizational events as needed.