Groundswell

Data Engineer

• Design and maintain scalable data architectures supporting complex enterprise applications and business processes
• Develop conceptual, logical, and physical data models, including data structures, relationships, schemas, and standards
• Design data solutions supporting Appian applications, including data stores, record types, data synchronization, and external data-source integrations
• Define how application data is stored, accessed, integrated, and exchanged across Appian and external systems of record
• Design and optimize relational database solutions and complex SQL queries
• Partner with Appian architects, developers, and integration teams on technical designs and complex data and application architecture challenges
• Evaluate existing data structures and recommend improvements to scalability, performance, maintainability, and data quality
• Design data integrations using APIs, web services, and other enterprise integration patterns
• Establish and promote standards for data integrity, security, access controls, metadata, and data governance
• Troubleshoot complex application, database, integration, and performance issues
• Participate in architecture and technical design reviews and ensure alignment with program and federal technology standards
• Provide technical guidance and mentorship to development teams and serve as a subject matter expert for data architecture and Appian-related data solutions
• Collaborate with federal client stakeholders to understand requirements, evaluate technical options, and communicate architecture recommendations

Data Architect, Appian

• Design, develop, and operate batch and streaming ETL/ELT pipelines that ingest data from multiple structured and semi-structured sources into secure cloud data platforms
• Onboard legacy data sources by reviewing schemas, mappings, interfaces, validation rules, ownership, and operational support procedures
• Build automated data-quality checks for completeness, accuracy, consistency, timeliness, and referential integrity, with actionable monitoring and alerting
• Automate deployments and pipeline operations using SOAP APIs, workflow orchestration, and environment-specific configurations
• Troubleshoot failures across distributed data systems using logs, metrics, lineage, and operational signals; communicate root causes and recovery plans clearly
• Collaborate with software engineers, cloud engineers, data scientists, security teams, architects, and customer stakeholders to translate mission needs into measurable data products

Data Engineer – Legacy Systems, AI Workflows

SDET

• Execute performance strategies for a federal program
• Validate system scalability and reliability
• Ensure the application meets defined performance and throughput requirements
• Design, develop, and execute performance test plans, scripts, and scenarios
• Analyze and report performance test results
• Analyze performance data and identify root causes across application, database, and infrastructure layers
• Gather throughput and SLA requirements from business stakeholders
• Analyze Appian logs and system information to diagnose performance issues
• Collaborate with technical and business stakeholders
• Support performance testing efforts in structured or regulated environments

Performance Appian Test Engineer – TS Clearance

• Lead end-to-end performance strategies for a federal program
• Execute performance testing strategies
• Validate system scalability and reliability
• Ensure the application meets defined performance and throughput requirements
• Design, develop, and execute performance test plans, scripts, and scenarios
• Analyze and report performance test results
• Analyze performance data and identify root causes across application, database, and infrastructure layers
• Gather throughput and SLA requirements from business stakeholders
• Analyze Appian logs and system information to diagnose performance issues
• Mentor or advise junior performance engineers
• Collaborate with technical and business stakeholders
• Support performance efforts in structured or regulated environments

Senior Performance Appian Test Engineer – TS Clearance

• Partner with project leads to develop testing strategies and performance goals for Appian projects
• Perform QA testing on Appian applications across several projects
• Create documentation and test scripts and record results
• Partner with project leads and developers to ensure issues are resolved
• Analyze bugs to identify trends
• Write user stories

QA Appian Test Engineer – TS Clearance

AI Engineer

• Take a defined technical approach and build it into production-quality functionality
• Participate in requirements discussions with business stakeholders to understand workflows, business needs, and desired outcomes
• Contribute ideas and alternative approaches during solution design
• Identify opportunities to simplify solutions or reduce the AI component where appropriate
• Build complete applications integrating AI capabilities with prompt and retrieval design, structured outputs, tool and function definitions, API integration, data handling, error handling, and user-facing functionality
• Build evaluation sets from real data, measure results, and accurately report findings
• Investigate failures, characterize causes, and implement fixes
• Support client demonstrations and proof-of-concepts
• Seek feedback and build understanding of client needs and AI best practices
• Instrument and document work, including logging, traceability, intended behavior, known limitations, validation methods, and failure handling
• Move between client delivery, internal product work, rapid proofs of concept, and internal enablement
• Raise blockers, unclear requirements, and untrusted results early
• Contribute reusable patterns, prompts, and evaluation harnesses to the team

Associate Applied AI Engineer

• Define business and technical requirements and the technical approach for AI capabilities
• Own capabilities end to end, including discovery, design, development, validation, deployment, and operation
• Work across client delivery, internal product development, rapid proofs of concept, and internal enablement
• Select appropriate AI patterns such as extraction, classification, summarization, retrieval, or agentic workflows
• Recommend simpler non-AI solutions when appropriate
• Define measurable success criteria, accuracy targets, human review thresholds, acceptance conditions, and failure definitions
• Build complete capabilities including prompt and retrieval design, structured outputs, tool and function definitions, API integration, data handling, error states, and user interfaces
• Build evaluation sets from real data, measure results, iterate, and determine deployment readiness
• Make architecture decisions considering quality, cost, latency, security, and authorization constraints
• Operationalize capabilities with governance, logging, traceability, monitoring, and post-launch optimization
• Help clients understand possibilities, anticipate needs, shape future work, and build proofs of concept
• Mentor engineers new to AI through code review, pairing, and guidance
• Maintain current knowledge of models, tooling, and techniques and develop reusable patterns
• Produce documentation covering solution behavior, limitations, validation, and incorrect-result handling

Applied AI Engineer

• Lead discovery with business and technical stakeholders to understand workflows, supported decisions, and acceptable results
• Define measurable success criteria, including accuracy targets, human review thresholds, acceptance conditions, and failure definitions
• Recommend non-AI solutions when rules, process changes, or improved interfaces are more appropriate
• Select appropriate AI patterns such as extraction, classification, summarization, retrieval, or agentic workflows
• Make and defend architecture decisions considering quality, cost, latency, security, and authorization
• Build complete AI-enabled capabilities, including prompt and retrieval design, structured outputs, tools, APIs, data handling, error states, and user interfaces
• Build evaluation sets from real data and iterate based on results
• Determine deployment readiness
• Operationalize capabilities in client environments with governance, logging, and traceability
• Produce documentation covering solution behavior, limitations, validation, and incorrect-result handling
• Monitor and optimize quality, cost, latency, and drift after launch
• Shape client roadmaps and demonstrate possibilities through working proofs
• Mentor engineers newer to AI and review their work
• Contribute reusable patterns to the team
• Move between client delivery, internal product work, rapid proofs of concept, and internal enablement

Senior Applied AI Engineer

• Build, configure and operate an AI-assisted platform that helps organizations understand, govern and move complex enterprise data
• Translate mission and customer data questions into clear objectives, success measures and evaluation criteria
• Profile, clean and characterize complex multi-source enterprise data
• Identify data gaps, conflicts, quality issues and relationships affecting downstream use
• Build and maintain metadata, data dictionaries and reference vocabularies for customer environments
• Configure and tune AI-assisted capabilities that match, classify, rank and validate data across systems
• Evaluate AI agents and language-model workflows for accuracy, grounding, consistency and failure modes
• Build test sets and track quality over time
• Analyze human review feedback to find systematic errors and improve platform performance
• Support data validation and reconciliation for trusted integrated or migrated data
• Partner with data, software and cloud engineers to move validated improvements into production with version control and measurable results
• Create visualizations, narratives and briefings for technical and non-technical stakeholders
• Handle sensitive data under access controls, governance and auditability
• Maintain reproducible code, experiments and documentation
• Contribute data science expertise to solution planning and proposal efforts as needed

Applied Data Scientist, AI Data Platforms

Backend Engineer

• Provide technical delivery leadership for a complex Workday implementation supporting an Intelligence Community customer
• Oversee and coordinate technical workstreams including Integration, Data Conversion, Security, environments, testing, and other technical delivery activities
• Define and coordinate technical requirements, dependencies, milestones, interfaces, plans, and deliverables across implementation teams
• Partner with the Solution Architect to align implementation with approved solution architecture and technical standards
• Partner with the Workday Delivery Lead to align technical activities with Workday Implementation Methodology and deployment schedule
• Lead cross-workstream technical discussions and resolve issues spanning multiple technical teams
• Keep technical teams and subcontractor resources aligned with implementation priorities and technical standards
• Identify technical risks, issues, and dependencies and ensure mitigation, ownership, and escalation
• Serve as a key technical interface with the customer's CIO organization and enterprise technology stakeholders
• Build relationships with customer architecture, infrastructure, cybersecurity, integration, data, and other technical organizations
• Coordinate technical requirements, decisions, dependencies, and readiness activities with customer stakeholders
• Translate Workday and implementation requirements into clear technical impacts and actions
• Communicate technical risks, decisions, and dependencies to technical and non-technical stakeholders
• Maintain visibility into ATO technical plans, milestones, deliverables, dependencies, and decisions affecting contract execution
• Understand deliverables and responsibilities across related programs and identify dependencies between efforts
• Coordinate with ATO technical teams, vendor technical teams, and program leadership to assess progress and impacts
• Assess downstream impacts of delays, changes, or technical decisions on milestones, workstreams, testing, and deployment readiness
• Communicate and manage technical dependencies affecting program commitments
• Support mitigation and recovery strategies when external technical dependencies affect contract execution
• Support technical design reviews, readiness reviews, and implementation checkpoints
• Ensure technical deliverables and artifacts meet quality, architecture, security, and customer requirements
• Coordinate dependencies involving environments, access, security, interfaces, data, testing, and deployment
• Provide technical status, risks, dependencies, and readiness assessments to the Workday Delivery Lead and Program Manager
• Support planning for implementation milestones, testing cycles, cutover, deployment, and operational transition