OverviewTISTA is seeking a highly capable Data & Solution Architect to lead the design, development, and deployment of modern data, analytics, and artificial intelligence solutions for enterprise and public-sector customers. This role bridges advanced data engineering, solution architecture, and production-grade AI, with a focus on building scalable Data Lakehouse capabilities using the Databricks Data Intelligence Platform and Palantir Foundry/AIP.
The Data & Solution Architect will work directly with customer teams to transform complex, ambiguous operational challenges into secure, governed, and high-value technical solutions. The role requires hands-on expertise in ingesting data from diverse source systems in near real time; progressively refining data quality; developing performant data products and marts; and enable team for deploying Generative AI, Retrieval-Augmented Generation (RAG), and multi-agent applications into production.
This is a customer-facing, delivery-oriented role that combines strategic architecture with hands-on implementation. The successful candidate will be comfortable embedding with client teams, including government environments, and leading solutions from initial discovery through production deployment and user adoption.
Responsibilities
Solution Architecture and Customer Engagement
- Translate high-level business, operational, and mission requirements into actionable technical roadmaps, architecture diagrams, backlog items, and production implementation plans.
- Serve as a trusted technical advisor to stakeholders at all levels, from operational users and technical teams to program leadership and C-level executives.
- Drive measurable customer adoption, operational improvement, technical training, and return on investment for delivered data and AI capabilities.
- Facilitate architecture reviews, technical workshops, demonstrations, design sessions, and iterative feedback cycles with customers and internal delivery teams.
- Lead end-to-end solution architecture for data engineering, analytics, collaborate with AI engineers for machine learning, and Generative AI initiatives using Databricks, Palantir Foundry, Palantir AIP, and associated cloud services.
- Embed directly with public-sector, and government customer teams to understand mission needs, operational processes, data challenges, and ambiguous business problems.
Data Lakehouse Engineering and Data Platform Development
- Architect and implement a scalable Data Lakehouse that ingests structured, semi-structured, and unstructured data from numerous enterprise and operational source systems.
- Design near-real-time, batch, streaming, API-based, event-driven, and file-based ingestion patterns appropriate to source-system capabilities and customer mission needs.
- Build and optimize scalable data pipelines using Python, PySpark, Spark SQL, and Delta Lake.
- Implement incremental ingestion, change data capture (CDC), schema evolution, data lineage, error handling, replay/reprocessing, and data reconciliation capabilities.
- Establish layered data architecture patterns that progressively improve data quality, standardization, enrichment, and usability across raw, curated, and consumption-ready data layers.
- Develop high-performance transformations that validate, cleanse, conform, aggregate, and denormalize data into product-specific star schemas, dimensional models, semantic layers, and data marts optimized for fast reporting and analytics.
- Modernize or refactor large single-step ETL processes into modular, resilient, observable, and maintainable pipeline architectures.
- Design and govern data assets across Delta Lake and Unity Catalog, including catalogs, schemas, tables, views, volumes, external locations, access controls, and data-sharing patterns.
- Establish data quality rules, profiling, validation, monitoring, lineage, observability, and remediation workflows across the data lifecycle.
Palantir Foundry and AIP Delivery
- Design, build, and deploy data pipelines, applications, and operational workflows within Palantir Foundry and Palantir AIP.
- Model business domains through Foundry ontology objects, relationships, actions, and operational workflows that enable users to make decisions and take action from trusted data.
- Build custom applications, analytical interfaces, and user-facing workflows that connect complex data products to frontline operational needs.
- Integrate data sources, AI services, and operational applications across Databricks and Palantir platforms where appropriate.
- Gather user feedback rapidly, identify edge cases, and iteratively enhance solutions in close partnership with customer teams.
- Communicate product feedback, integration needs, and recurring customer requirements to internal engineering, product, and delivery teams.
Generative AI, Machine Learning, and MLOps
- Collaborate with team of AI engineers for
- Design, build, and deploy end-to-end Generative AI applications directly within the Databricks Data Intelligence Platform and integrated customer environments.
- Develop Retrieval-Augmented Generation (RAG) architectures that securely ground large language models in governed enterprise data, documents, policies, and knowledge repositories.
- Architect and implement multi-agent systems and AI-enabled workflows for research, analysis, decision support, automation, and user assistance.
- Integrate LLM and AI APIs, vector search, embedding models, prompt orchestration, model-serving endpoints, and user-facing analytical applications.
- Establish automated evaluation frameworks, including LLM-as-a-Judge, benchmark datasets, human-in-the-loop review, safety evaluation, and quality measurement processes.
- Support hyperparameter tuning, experiment tracking, model versioning, model registry management, drift monitoring, performance monitoring, and retraining strategies.
- Apply responsible AI principles, including security, privacy, traceability, access controls, model evaluation, and human oversight appropriate to the customer environment.
- Implement CI/CD practices for data pipelines, prompt engineering, model deployment, configuration management, and application releases.
Security, Governance, and Production Operations
- Guide customers in establishing practical data security, access-control, governance, privacy, auditability, and compliance patterns.
- Implement role-based and attribute-based access controls, least-privilege permissions, secure data-sharing practices, and environment-separation strategies.
- Design production-grade CI/CD pipelines for infrastructure, data pipelines, analytical applications, and AI/ML workloads.
- Establish monitoring, alerting, logging, runbooks, incident response procedures, and operational support models for deployed solutions.
- Ensure solutions meet applicable organizational, contractual, and government security requirements.
Qualifications
- 7+ years of progressive experience in data engineering, data platform engineering, software engineering, data architecture, or solution architecture.
- Demonstrated experience designing and implementing scalable data ingestion, transformation, integration, and quality-management pipelines.
- Strong hands-on experience with Databricks, Apache Spark, PySpark, Spark SQL, Delta Lake, and Lakehouse architecture patterns.
- Strong proficiency in Python. Working proficiency in one or more of Scala, Java, JavaScript, or TypeScript is preferred.
- Experience designing and deploying solutions in one or more major cloud environments: AWS, Microsoft Azure, or Google Cloud Platform.
- Experience with dimensional modeling, star schemas, denormalized reporting models, data marts, semantic layers, and performance optimization.
- Knowledge of developing or deploying machine learning, LLM, RAG, Generative AI, or AI-enabled applications; familiarity with MLOps practices is preferred.
- Ability to work directly with customers, clarify ambiguous requirements, lead technical discussions, and deliver usable production solutions.
- Excellent written, verbal, presentation, and stakeholder-management skills; ability to explain complex technical concepts to technical and nontechnical audiences.
- Willingness and flexibility to travel or work on-site with customer teams as required.
Preferred Qualifications
- Hands-on experience with Palantir Foundry, Palantir AIP, ontology modeling, Foundry pipelines, operational applications, or Palantir deployment patterns.
- Deep expertise in Apache Spark performance tuning, Spark runtime internals, distributed computing, and large-scale data processing.
- Experience with real-time data technologies such as Kafka, Kinesis, Event Hubs, Delta Live Tables, Structured Streaming, or change data capture frameworks.
- Experience with Databricks Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog, Lakeflow, Databricks Workflows, or Databricks SQL.
- Knowledge of implementing RAG architectures, agentic workflows, vector databases, prompt management, LLM evaluation frameworks, and model monitoring.
- Familiarity with infrastructure-as-code and DevOps tooling such as Terraform, GitHub Actions, GitLab CI/CD, Azure DevOps, Jenkins, or similar platforms.
- Experience supporting federal, defense, intelligence, healthcare, financial services, or other regulated environments.
- Active or ability to obtain and maintain a government security clearance, if required by the customer program.
- Databricks certifications, including Databricks Certified Data Engineer, Databricks Certified Data Analyst, Databricks Certified Machine Learning Professional, or Databricks Certified Generative AI Engineer.
- Cloud certifications from AWS, Microsoft Azure, Google Cloud, or Palantir.
Core Competencies
- Architectural leadership: Balances long-term platform strategy with practical, incremental delivery.
- Hands-on execution: Comfortable moving from whiteboard design to code, configuration, testing, deployment, and operational support.
- Mission and customer focus: Understands the operational context behind data and AI requirements and prioritizes outcomes that matter to users.
- Analytical problem-solving: Thrives in complex environments with incomplete information, changing requirements, and multiple stakeholder perspectives.
- Rapid iteration: Builds prototypes quickly, incorporates user feedback, and hardens successful prototypes into secure, reliable production capabilities.
- Technical rigor: Applies sound engineering practices in data quality, performance, security, testing, governance, and maintainability.
- Collaboration: Works effectively across customer teams, engineering teams, data scientists, security organizations, program leadership, and executive stakeholders.
Work Environment
This position may require regular collaboration with customer personnel at enterprise offices, government facilities, or secure environments. The role is suited to a technically strong, adaptable professional who can take ownership of challenging data problems, operate effectively in customer-facing settings, and deliver measurable mission and business outcomes.
Education:
- Bachelor’s degree in Computer Science, Information Systems, Systems Engineering, or a related field.
- Master’s degree preferred.
Clearance:
- Tier 2 / Moderate Risk Background Investigation; ability to obtain a VA PIV credential.
Location:
- Rockville, MD / Remote (CONUS).
- Up to 15% CONUS travel.
Pay Range:
- The suggested pay for this position ranges from $171,546 to $189,875.
- The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience, and location.
- Also, certain positions are eligible for additional forms of compensation, such as bonuses.
- TISTA associates are eligible to participate in our comprehensive benefits plan! More information can be found here: https://tistatech.com/working-at-tista/
