Data/Systems Engineer (Bid role starting in 8-10 weeks) - TS/SCI w/ Poly

About the role

The Sponsor requires Data Engineering support to evaluate, optimize, and implement robust data infrastructure that enables reliable, accessible, and scalable data delivery across the organization.

Responsibilities

• Work collaboratively with data consumers, technical teams, leadership, and stakeholders to assess current data pipelines, identify gaps in data accessibility and reliability, and architect solutions that establish trusted data foundations.
• Apply engineering best practices to implement proper data modeling and integration patterns, ensuring data quality and observability throughout pipelines, and creating maintainable infrastructure that supports analytics, reporting, and operational use cases.
• Conduct comprehensive assessments of existing data pipelines, infrastructure, and data flows including integrations with operational systems like ServiceNow, network management platforms, and business applications to identify technical debt, bottlenecks, and reliability issues.
• Evaluate current data architecture against industry best practices and organizational needs; develop technical recommendations and roadmaps for data infrastructure improvements.
• Design, build, and maintain production-grade data pipelines using orchestration tools such as Airflow or Prefect.
• Develop robust ETL/ELT processes from diverse sources including SaaS platforms, network management systems, databases, APIs, files, and streams.
• Build API integrations handling authentication (OAuth, API keys, and Single Sign-On (SSO)), rate limiting, pagination, retry logic, and error handling.
• Extract data from systems not designed for export; reverse-engineer undocumented data structures and relationships.
• Handle semi-structured data (JSON and XML); transform into structured datasets with consistent schemas.
• Design dimensional models, data warehouses, and data marts following industry methodologies.
• Create conceptual, logical, and physical data models optimized for query performance and storage efficiency.
• Implement slowly changing dimensions and other data warehousing patterns.
• Establish naming conventions, data standards, and modeling best practices.
• Implement comprehensive data quality checks, validation rules, and automated monitoring with alerting.
• Build error handling, failure recovery, logging, and observability into all processes.
• Optimize pipelines for performance, cost, and resource utilization.
• Develop reusable components and frameworks; refactor legacy pipelines for reliability.
• Build and maintain data infrastructure on cloud platforms (Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP)) using infrastructure-as-code using Terraform and CloudFormation.
• Implement CI/CD pipelines, version control (Git), and automated testing frameworks.
• Manage database performance tuning, indexing, partitioning, and capacity planning.
• Establish backup, recovery, security controls, access controls, and compliance measures.
• Partner with analysts, software developers, and business stakeholders to translate requirements into technical solutions.
• Create comprehensive documentation for systems, processes, and integrations.
• Provide technical guidance on data availability and proper usage; enable self-service access.
• Truss pipeline failures, performance issues, and data discrepancies; perform root cause analysis.

Requirements

• Demonstrated experience designing, building, and maintaining production data pipelines using orchestration tools such as Apache Airflow or similar.
• Demonstrated experience with SQL skills including complex queries, optimization, and performance tuning across multiple database platforms.
• Demonstrated experience integrating data from Sponsor SaaS platforms and operational systems via APIs, including handling authentication, pagination, and rate limiting.
• Demonstrated experience working with semi-structured data (JSON and XML) from API responses and transforming into structured datasets.
• Demonstrated experience with developing robust API integrations with proper error handling and retry logic.
• Demonstrated experience working with systems that have limited documentation or vendor-specific data models.
• Demonstrated experience with dimensional modeling and data warehouse design patterns.
• Demonstrated proficiency in Python for data engineering including working with data processing libraries.
• Demonstrated experience with cloud data platforms such as AWS, Azure, or GCP, including data services and infrastructure.
• Demonstrated experience implementing ETL/ELT processes from diverse data sources.
• Demonstrated experience with version control (Git) and software engineering best practices.
• Demonstrated experience with strong problem-solving and troubleshooting skills for complex data pipeline issues.
• Demonstrated experience implementing data quality checks and validation frameworks.
• Demonstrated experience translating business requirements into technical data solutions.
• Demonstrated experience in having a proven track record of delivering reliable, scalable data infrastructure.

Benefits

Leading Path is an award-winning Information Technology and Management Consulting firm focused on providing solutions in process, technology, and operations to our government and Fortune 500 clients. We offer a professional and family friendly work environment with a strong work-life balance. Leading Path provides a comprehensive and competitive benefits package including fully paid medical/dental/vision premiums, generous PTO, 11 Paid Holidays, 6% 401K contribution, annual training and tuition reimbursement, SPOT Award bonuses, regular team events, opportunities for professional growth and advancement and much more!