Staff Software Engineer (Data Platform)
First seen by Alion on Oct 30, 2026.
About Bizee:
Bizee helps entrepreneurs across the US start and grow their businesses, with services spanning company formation and ongoing compliance. We make complex steps easier to navigate so business owners can focus on building what comes next.
About the role:
Bizee helps entrepreneurs start and grow their businesses. We're looking for a hands-on software engineer to build the data platform used across the organisation. You'll own the systems that make data available to products, operations, marketing, finance and AI applications, so teams can build services, automate work, experiment and make better decisions.
You'll build the paths from source data to action and back to observed outcomes. That includes pipelines, shared data models and APIs that give people and AI agents the context they need, plus feedback loops that record what happened and support evaluation and improvement. You'll own the work from understanding the need through implementation, deployment and adoption.
AI coding tools and agents will be a core part of how you build. You'll use them to investigate systems, develop and test changes, and accelerate delivery while applying your own engineering judgment and verifying the result.
At L5, you'll be able to start where the problem, requirements or ownership are unclear, investigate the existing systems and decide what to tackle first. You'll stay directly involved in implementation, debugging and rollout, and help other engineers deliver alongside you. Success means working systems and reliable data that teams use.
What you'll do:
Work directly with teams across the organisation to understand what they need to build or automate. Turn recurring needs into shared data services and interfaces that teams can use without bespoke integrations or repeated manual work.
Build pipelines and services that collect, transform and deliver data to operational applications, analytical systems and AI workflows. Handle retries, duplicate events, late-arriving data and replay so systems can recover reliably.
Capture business events and state changes for customers, orders, payments, subscriptions and fulfillment. Establish identifiers, schemas and history that let systems connect activity across the customer journey.
Build closed feedback loops that connect inputs, decisions and actions to their outcomes. Make that evidence usable for experiments, model and agent evaluation, and improvements to automated workflows.
Build quality checks, lineage and monitoring into the platform. Detect missing, stale or inconsistent data, trace failures to their source and prevent bad inputs from silently affecting customer experiences or automated decisions.
Connect legacy and new systems while improving the underlying data models and contracts. Validate migrations and changes without disrupting the services and workflows that depend on them.
Build datasets, APIs and retrieval interfaces that give teams and AI agents access to current and historical context. Make permissions, provenance and freshness clear, and capture outcomes so downstream systems can learn what worked.
Use AI tools throughout implementation, debugging and testing. Own deployment, performance, cost and operational documentation, and leave systems that colleagues can understand and maintain.
What you'll bring:
Strong software engineering fundamentals and experience building and operating production systems, including testing, APIs, failure handling and maintainable design.
Strong SQL and practical experience with data modeling, pipelines and data-serving systems. You can design data for operational and analytical use, trace it to its source and reason about quality, history and consistency.
Strong Python skills and experience using it to build and operate production data pipelines, integrations and services, with testing and maintainable code. Go is our primary backend language; familiarity with it is useful when working with the services that produce and consume data.
Experience with event-driven or distributed systems, including schema evolution, idempotency, replay and consistency. You can choose between batch and streaming approaches based on the need.
A track record of delivering production systems from ambiguous starting points. You can show how you found the problem, chose a practical approach and implemented it, including the difficult debugging and trade-offs.
Practical use of AI coding tools and agents, backed by technical judgment, code review and testing. You understand and remain accountable for generated work.
The ability to learn business domains, question assumptions and explain trade-offs to technical and nontechnical colleagues. Clear communication in English and ownership through delivery and adoption.
Nice to have:
Experience building shared data platforms, operational data APIs, cloud data infrastructure or migrations between legacy and new systems.
Experience with change data capture, orchestration, transformation frameworks, data lineage or automated data-quality checks.
Experience with feedback loops for AI systems, retrieval, experiment infrastructure or connecting model and agent decisions to observed outcomes.
What we offer
Competitive compensation 30,000-42,000 PLN/month, stock options, and an annual bonus.
A flexible benefits allowance to spend on what matters to you.
An office in the city center and a friendly, non-corporate atmosphere.
Real ownership and the opportunity to make a visible impact.
Tech stack
- Python
- SQL
- Data modeling
- Distributed systems
- AI