Principal Product Software Engineer

Wolters Kluwer

Wolters Kluwer delivers expert solutions that combine deep domain knowledge with advanced technology, enabling professionals to make better decisions, stay compliant with complex regulations, and improve outcomes. Its portfolio spans industries such as Health, Tax & Accounting, Governance, Risk & Compliance (GRC), and Legal & Regulatory. Innovation, digital transformation, and responsible business practices are core to Wolters Kluwer’s long-term strategy.

Twinfield, part of Wolters Kluwer, is a cloud-based accounting software solution designed for businesses and accounting professionals. It streamlines financial administration by automating bookkeeping, invoicing, cash flow management, and reporting in real time. Twinfield is known for its strong compliance capabilities, security, and integration options, making it well suited for SMEs, international organizations, and accounting firms. You will be part of the ‘Tech B.V.’ team, which focuses on development, service and delivery of technology solutions that support Twinfield’s digital products and services.

Why this role exists

We’re looking for a Principal Engineer who makes engineering teams meaningfully better.

Not by introducing the newest framework or rewriting everything, but by improving how problems are understood, how systems are designed, and how value moves from idea to production. You’ve helped organizations ship faster and with higher quality by changing engineering practices, not just codebases.

This role is for someone who operates at the intersection of architecture, engineering effectiveness, and modern AI-enabled development, with a strong bias toward rigor, clarity, and measurable outcomes.

What you’ll do

Own the hard problems

• Take loosely defined product or platform problems and turn them into clear problem statements, constraints, and success criteria.
• Rationalize complex technical architectures into coherent, reviewable designs that engineers can execute incrementally.
• Make principled tradeoffs between speed, quality, and long term maintainability—and explain those tradeoffs clearly.

Raise the bar on engineering practices

• Identify friction in the SDLC and help teams remove it through better design practices, clearer specifications, improved testing strategies, and tighter feedback loops.
• Lead the adoption of specification driven development: explicit contracts, well-defined interfaces, and testable requirements.
• Promote development workflows where design, validation, and implementation are deliberate phases, and production code reflects understood decisions.

Accelerate delivery (measurably)

• Help teams deliver faster by improving how work is shaped, not by asking people to work harder.
• Introduce practices that lead to observable improvements in:

• Lead time and cycle time
• Change failure rate
• Defect escape rates
• Confidence in releases

Use data where it helps, judgment where it matters.

Enable agentic development—responsibly

• Experiment with and enable agentic development techniques across the SDLC, including requirements refinement, design exploration, test generation and validation, documentation, and maintenance.
• Ensure that AI and agent assisted development is grounded in explicit intent—using specifications, contracts, and tests as primary inputs—so automation amplifies engineering judgment rather than substituting for it.
• Establish guardrails that keep systems understandable, changes reviewable, and intent preserved over time.

Build and evolve cloud-native systems (modernization, not just migration)

• Lead and contribute to the design and delivery of cloud-native applications: observable, resilient, secure by default, and independently deployable.
• Guide teams through legacy modernization efforts that result in true cloud-native characteristics—clear service boundaries, independent deployability, container readiness, and appropriate use of managed platform services—not simply moving existing systems to cloud infrastructure.
• Help teams make pragmatic hosting decisions (IaaS vs. PaaS, VMs vs. containers/Kubernetes) based on system constraints, maturity, and desired outcomes.

Build API first systems across protocols

• Design, build, and evolve APIs with strong contract discipline, backward compatibility strategies, and a focus on consumer experience.
• Bring broad experience consuming and producing multiple API styles and protocols, including:

• SOAP / WCFstyle services
• RESTful APIs (including hypermedia / HATEOAS where appropriate)
• gRPC

• Understand and articulate the tradeoffs between these approaches—latency, coupling, discoverability, versioning, tooling, and evolution—and guide teams on when each is appropriate.

Build AI powered products (not demos)

• Contribute to systems that integrate AI/ML capabilities into real, customer facing products, including (but not limited to) GenAI features and agents.
• Help teams reason about model behavior, failure modes, system boundaries, and operational risks.
• Ensure AI features are built with security, observability, and long term operability in mind.

Lead without authority

• Act as a technical multiplier across teams.
• Mentor senior and staff engineers through design reviews, architectural discussions, and hands-on collaboration.
• Influence through clarity, reasoning, and credibility—not mandates.

Our technical context (for orientation, not gatekeeping)

Our systems span a mix of:

• Backend technologies including Microsoft .NET (multiple versions and languages).
• Frontend technologies such as JavaScript / TypeScript with frameworks like Angular and React.
• Infrastructure across both IaaS and PaaS, including virtual machines and Kubernetes based platforms, all hosted on Microsoft Azure.

You do not need to have worked with all of these specifically. Experience with similar ecosystems and the ability to reason across them matters more than matching keywords.

How you work

We’re looking for someone who:

• Thinks in systems, but delivers in increments.
• Writes design documents that engineers actually want to read.
• Treats testing, documentation, and observability as first-class engineering concerns.
• Is comfortable saying “this is good enough for now” and “this needs to be fixed properly.”
• Knows when to experiment and when to standardize.

Experience we care about (not checkboxes)

We care less about which technologies you’ve used and more about what changed because you were there. Strong signals include:

• Leading or heavily influencing architectural decisions in complex systems.
• Improving engineering throughput and quality through better practices—not rewrites.
• Leading modernization efforts that moved systems toward genuine cloud-native outcomes.
• Enabling AI assisted and agentic workflows with clear intent, specifications, and guardrails.
• Demonstrated breadth across API styles and the judgment to choose and evolve them appropriately.

What success looks like

After you’ve been here for a while:

• Teams are clearer on what they’re building and why before they start coding.
• Delivery is faster, releases are calmer, and failures are easier to reason about.
• Engineers trust the systems they work on—and the process they use to change them.
• AI and agents are used where they create leverage, with intent preserved and outcomes measurable.

What do we offer?

• 25 vacation days (based of 40 hours).
• Wolters Kluwer refunds 50% of your pension contributions.
• An informal working environment and with multiple events planned each year.
• Coffee from freshly toasted fair-trade beans and every day delicious fresh fruit.
• Every day a lunch buffet is provided to enjoy with your colleagues and free of cost.