Data & Solution Architect (Databricks and Palantir Platforms)

TISTA Science and Technology Corporation•Rockville, MD

3d•Remote

About The Position

TISTA 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. The role combines advanced data engineering, solution architecture, and production-grade AI, with an emphasis on scalable Data Lakehouse capabilities using Databricks and Palantir Foundry/AIP. The architect works directly with customer teams to turn complex or ambiguous operational challenges into secure, governed technical solutions. Work includes ingesting data from diverse sources in near real time, improving data quality, developing performant data products and marts, and enabling production Generative AI, Retrieval-Augmented Generation (RAG), and multi-agent applications. This is a customer-facing, delivery-oriented role combining strategic architecture with hands-on implementation. The successful candidate will be comfortable embedding with client teams, including in government environments, and leading solutions from discovery through production deployment and user adoption. The position may require collaboration at enterprise offices, government facilities, or secure environments. It is based in Rockville, Maryland, or remote within the continental United States, with up to 15% CONUS travel. The position requires a Tier 2 / Moderate Risk Background Investigation and the ability to obtain a VA PIV credential. A bachelor’s degree in Computer Science, Information Systems, Systems Engineering, or a related field is required; a master’s degree is preferred. The suggested pay range is $171,546–$189,875, with actual offers dependent on skills, qualifications, experience, and location. Certain positions may also be eligible for bonuses.

Requirements

7 or more 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.
Experience designing and deploying solutions in at least one major cloud environment: AWS, Microsoft Azure, or Google Cloud Platform.
Experience with dimensional modeling, star schemas, denormalized reporting models, data marts, semantic layers, and performance optimization.
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, including the ability to explain technical concepts to technical and nontechnical audiences.
Willingness and flexibility to travel or work on-site with customer teams as required.
Bachelor’s degree in Computer Science, Information Systems, Systems Engineering, or a related field.
Tier 2 / Moderate Risk Background Investigation and ability to obtain a VA PIV credential.

Nice To Haves

Working proficiency in one or more of Scala, Java, JavaScript, or TypeScript.
Knowledge of developing or deploying machine learning, LLM, RAG, Generative AI, or AI-enabled applications; familiarity with MLOps practices.
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 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 government security clearance or ability to obtain and maintain one, 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.
Master’s degree.

Responsibilities

Translate business, operational, and mission requirements into technical roadmaps, architecture diagrams, backlog items, and production implementation plans.
Advise stakeholders ranging from operational users and technical teams to program leadership and C-level executives.
Drive 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.
Lead end-to-end solution architecture for data engineering and analytics initiatives, and collaborate with AI engineers on machine learning and Generative AI initiatives using Databricks, Palantir Foundry, Palantir AIP, and associated cloud services.
Embed with public-sector and government customer teams to understand mission needs, operational processes, data challenges, and ambiguous business problems.
Architect and implement scalable Data Lakehouse capabilities that ingest structured, semi-structured, and unstructured data from enterprise and operational systems.
Design near-real-time, batch, streaming, API-based, event-driven, and file-based ingestion patterns.
Build and optimize data pipelines using Python, PySpark, Spark SQL, and Delta Lake.
Implement incremental ingestion, change data capture, schema evolution, lineage, error handling, replay and reprocessing, and data reconciliation.
Establish layered data architecture patterns that improve data quality, standardization, enrichment, and usability across raw, curated, and consumption-ready data layers.
Develop transformations and data models, including product-specific star schemas, dimensional models, semantic layers, and data marts optimized for reporting and analytics.
Modernize or refactor large single-step ETL processes into modular, resilient, observable, maintainable pipeline architectures.
Design and govern Delta Lake and Unity Catalog assets, 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.
Design, build, and deploy data pipelines, applications, and operational workflows in Palantir Foundry and Palantir AIP.
Model business domains using Foundry ontology objects, relationships, actions, and operational workflows.
Build custom applications, analytical interfaces, and user-facing workflows that connect data products to operational needs.
Integrate data sources, AI services, and operational applications across Databricks and Palantir where appropriate.
Gather user feedback, identify edge cases, and iteratively enhance solutions with customer teams.
Communicate product feedback, integration needs, and recurring customer requirements to internal engineering, product, and delivery teams.
Design, build, and deploy Generative AI applications within Databricks and integrated customer environments.
Develop RAG architectures that securely ground 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.
Support hyperparameter tuning, experiment tracking, model versioning, model registry management, drift and performance monitoring, and retraining strategies.
Apply responsible AI principles, including security, privacy, traceability, access controls, model evaluation, and appropriate human oversight.
Implement CI/CD practices for data pipelines, prompt engineering, model deployment, configuration management, and application releases.
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, and environment separation.
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.
Ensure solutions meet applicable organizational, contractual, and government security requirements.