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Kaluza

Billing automation and grid optimization software

Director Quality & Release Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Proven track record leading a QA or quality engineering function of significant scale, including embedded domain leads and direct management of practitioners.
  • Direct experience managing engineers building tooling or automation infrastructure, not only QA practitioners executing test plans.
  • Experience delivering a meaningful QA transformation, such as AI adoption, automation maturity uplift, or defect governance overhaul.
  • Experience in a multi-client or multi-product platform environment, managing quality outcomes across concurrent delivery streams.
  • Experience owning release governance in a complex, multi-stream delivery context, including go/no-go accountability.
  • Strong grounding in test strategy, automation frameworks, and continuous integration and continuous delivery integration, sufficient to credibly lead engineers building these capabilities.
  • Working knowledge of large language models and AI tooling in a QA context, including test generation, defect triage, coverage analysis, and synthetic test data.
  • Understanding of test data management principles and environment data integrity challenges at scale.
  • Ability to evaluate build-versus-buy options for tooling and assess AI testing platforms critically.
  • Ability to set strategy and be accountable for outcomes, not only own process.
  • Ability to operate at programme level with senior engineering and product stakeholders.
  • Experience leading distributed, multi-timezone quality functions.
  • Ability to translate quality metrics into business outcomes and make the case for investment in tooling and governance.
  • Demonstrated experience leading practice transformation in an engineering context.
  • Ability to guide people through transitions that challenge established ways of working.
  • Comfort with ambiguity and maintaining direction while details are being worked out.
  • Familiarity with observability, production monitoring, and shift-right quality practices.

Responsibilities

  • Own and evolve the quality strategy across delivery streams, aligned to programme objectives and client service-level agreements.
  • Define and maintain release governance frameworks, including release-readiness criteria, risk acceptance, and sign-off authorities.
  • Set quality standards and coverage expectations, and ensure QA leads translate them into domain-level test strategies.
  • Drive defect governance at programme level, including internal and client-facing service-level agreement compliance.
  • Represent quality and risk in senior leadership, architecture, and product forums.
  • Directly manage a team of engineers building and maintaining bespoke automation tooling that leverages AI and large language model capabilities.
  • Own the test data management strategy, including AI-assisted synthetic data generation and environment data integrity.
  • Evaluate and select off-the-shelf AI testing tools, and define governance frameworks for their use and validation.
  • Maintain governance gates as AI participation in the product development lifecycle grows, ensuring accountable humans remain involved in risk acceptance and production judgment.
  • Establish tooling standards that QA leads can adopt across domains without creating a central bottleneck.
  • Lead, develop, and performance-manage QA leads embedded across domains, and define the function's operating model and career framework.
  • Maintain a clear accountability split: domain QA leads own test strategy and tooling choices, while this role owns the standards they follow.
  • Drive consistency across delivery streams while enabling QA leads to work quickly within defined guardrails.
  • Build a quality engineering culture focused on quality rather than bug finding.
  • Lead the transformation of QA practice from script maintenance to intelligent, risk-based, AI-assisted quality engineering.
  • Drive upskilling across the function in AI tooling, context engineering, and agent-assisted workflows.
  • Manage resistance and build confidence in new ways of working while guiding the team through change.
  • Communicate quality outcomes to senior stakeholders in business terms, including escaped defect rates, release stability, and risk posture.
  • Own the end-to-end release quality process, including environment strategy, regression coverage, go/no-go decisions, and post-release monitoring.
  • Embed shift-left practices by introducing quality gates early in the product development lifecycle, not only at release.
  • Manage the balance between delivery velocity and release stability, particularly as volumes of AI-generated code increase.

Desired Qualifications

  • Exposure to energy, utilities, billing, or similarly regulated complex domains is a plus but not essential.

About the Company

Kaluza

Billing automation and grid optimization software

Kaluza provides an intelligent energy platform for utilities to manage the shift toward a decentralized, decarbonized grid, automating core tasks like billing and customer care. The product combines software tools with end-to-end services that automate back-office processes and enable demand-side management of flexible devices such as EV chargers and smart thermostats, coordinating energy use with renewable supply. It differentiates itself by offering an end-to-end package, including in-home hardware installation, and by partnering with major providers like AGL to support practical decarbonization. The goal is to help energy retailers and utilities transition to a sustainable, customer-focused energy system while maintaining grid stability and reducing operating costs.

Company size
501-1,000
Company stage
Series A
Total funding
$100M
Headquarters
London, United Kingdom
Founded
2019

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