Data Engineer with Security Clearance

Make the data trustworthy. A financial system can have a beautiful interface, clean APIs, and perfectly healthy services. If the data underneath it is wrong, none of that matters. Pioneering Evolution builds software supporting federal acquisition and financial management — the planning, budgeting, tasking, execution, and decision-making processes behind billions of dollars in resources. We are now building SyncCore™ and SyncPoint™, a federated enterprise platform intended to connect those processes, systems, data, and decisions across complex mission environments.

That means solving some genuinely difficult data problems. Different systems describe the same things differently. Legacy data rarely arrives clean. Financial information has to be traceable back to its source. Schema changes have consequences. Integrations fail. Historical records matter. And when an analyst, application, or AI capability asks a question, the answer has to come from data we can trust. We need a Data Engineer who wants to own those problems.

What you would actually work on: SyncCore is our Mission Operating System — the secure, event-driven digital backbone for identity, integration, canonical data, governance, auditability, and interoperability. Built on SyncCore, SyncPoint is a modular application ecosystem composed of independently deployable modules supporting requirements management, budgeting, task planning, resource allocation, execution, and decision support. The Data Engineer helps make the information flowing through that ecosystem coherent.

You will design relational and canonical data models, build pipelines that bring information in from external and predecessor systems, develop transformations between different representations of the same business concepts, and create the validation and reconciliation mechanisms that tell us whether the result is actually correct. Some days that means designing a PostgreSQL schema for a difficult financial domain.

Other days it means figuring out why two source systems disagree, building a repeatable migration for years of legacy data, optimizing a query that suddenly became expensive at production scale, or designing an integration that has to survive partial failures and retries. You will also help establish how we handle history, lineage, provenance, auditing, retention, and data quality so that information does not simply appear in SyncPoint — we can explain where it came from, how it changed, and why we trust it. This is engineering work, not primarily reporting or dashboard development.

You will write SQL and software, design data structures and interfaces, troubleshoot production behavior, and work directly with the engineers building the applications that depend on your data. Where data meets the rest of the platform The Chief Engineer owns system architecture and engineering standards. You will be the data specialist helping shape how that architecture handles persistence, canonical models, integration, lineage, and data movement — and you should be willing to challenge an approach when the behavior of the data tells us something important.

Software Engineers will depend on you for durable data models and integration patterns rather than creating a different interpretation of the same domain in every service. You will work closely with the AI/ML Engineer to make trusted application data usable for analytics, retrieval, and generative-AI capabilities. That includes metadata, provenance, retrieval patterns, and access controls that ensure an AI capability can retrieve only information the requesting user is authorized to see.

You may also work with analysts, product teams, DevSecOps, and cybersecurity when the data crosses system or security boundaries. You do not own every one of those functions. You do own making sure the data solution works. The current environment: Our platform uses PostgreSQL as a primary relational data store, with Cosmos DB in portions of the architecture. The broader implementation includes C# and modern .NET, SQL, Python, REST APIs, JSON-based integrations, asynchronous and event-driven messaging, Docker, CI/CD, and Azure / Azure Government.

The applications are distributed and multi-tenant. Data moves through APIs, pipelines, events, application services, migrations, and external integrations. That creates interesting failure modes. A pipeline that ran successfully may still have produced the wrong data. An event may arrive twice. A schema change may be backward compatible for one service and destructive for another. A migration may be technically complete while silently dropping relationships that matter to the business. We want someone who thinks about those things before production teaches us the lesson.

What we actually require Typically 5+ years of professional experience in data engineering, database engineering, backend engineering, or comparable work. Strong SQL and relational-data fundamentals, including data modeling, constraints, normalization, indexing, and performance. Significant hands-on experience with PostgreSQL or another enterprise relational database, including query optimization and troubleshooting. PostgreSQL experience is strongly preferred, but deep database engineering matters more than a logo on your résumé.

Experience building and operating production ETL/ELT pipelines, data migrations, or complex data integrations. Experience designing processes for validation, reconciliation, deduplication, and data quality rather than assuming successful execution means correct results. Experience evolving schemas and production data safely across multiple environments. Professional programming experience with C#/.NET, Python, Java, or another general-purpose language.

Our application platform is primarily .NET, so you need to be comfortable working with that codebase, but we are not screening out a strong data engineer because your last team used Python. Experience building or consuming REST APIs and service integrations. Working knowledge of source control, automated testing, and modern CI/CD practices. The ability to reason across systems when the defect reported in one application was actually created three transformations upstream. Clear technical communication and documentation.

Data mappings, lineage, assumptions, and system-of-record boundaries need to be understandable by someone other than the person who created them. A bachelor's degree in Computer Science, Data Engineering, Information Systems, Mathematics, or a related discipline is useful. Equivalent professional experience is absolutely acceptable. Particularly useful, not an entrance exam: Azure or Azure Government. RabbitMQ, Azure messaging technologies, Kafka, or other event-driven architectures. Large-scale legacy data migrations. Financial, ERP, planning, acquisition, or program-management data.

Navy ERP, PIEE, EDW, PPBE, or related federal financial-management environments. Multi-tenant systems and complex authorization models. CUI, IL4, or other regulated federal environments. Vector retrieval, embeddings, RAG, or supporting production AI/ML systems. Cosmos DB or other non-relational data stores. You do not need to arrive knowing Navy financial systems. We can teach you what PPBE means, how Navy ERP represents a particular transaction, and why two government systems have different names for what appears to be the same thing. We cannot quickly teach deep data-engineering judgment.

That is what we are hiring for. Who thrives here: The strongest Data Engineers are professionally suspicious of data. They want to know where a number came from. They notice that two tables disagree when everyone else is happy the import completed. They want constraints where constraints belong, tests around transformations that matter, and clear ownership of what constitutes the authoritative source. They also understand that elegance is not the objective by itself. Sometimes the right answer is a beautifully normalized relational model.

Sometimes an analytical projection needs to be intentionally denormalized. Sometimes the source system is ugly and cannot be changed. Sometimes preserving provenance matters more than making the incoming data look clean. We want someone who can make those tradeoffs deliberately and explain why. We move fast, and we would rather create something special than something that merely loads without error. That takes a dynamic team, and ours is small enough that a good data decision reaches production quickly — surrounded by genuinely talented engineers who will notice you made it.

You will be working alongside strong software engineers, a Chief Engineer, technical leadership, and specialists in AI/ML, QA, cybersecurity, and DevSecOps, close enough that your expertise materially affects how the platform evolves. If you like turning complicated, inconsistent information into something the rest of a system can confidently depend on, this is a good place to do it. Clearance, citizenship, and location: U.S. citizenship is required. No exceptions. An active clearance is not required to apply. Active Secret is welcome.

Otherwise, interim Secret eligibility is required and PE sponsors — we will walk you through it. Hybrid, a few days a week at our Arlington HQ, 2550 S. Clark Street. What we offer: $100,000 – $155,000 based on demonstrated technical depth, scope of prior responsibility, and interview performance. Plus paid time off, 10 paid holidays, medical, dental, and vision insurance, company-paid life and AD&D, company-paid short- and long-term disability, 401(k) with company contribution, legal assistance, tuition reimbursement, and continuing education opportunities.

And the part specific to this role: the data architecture is still taking shape. You are not joining after every schema, integration boundary, canonical model, and data pipeline has already been decided. You will have the opportunity to influence how a new platform treats one of its most important ass