Why this role?

We need someone who has built the platforms and infrastructure that enable quality at scale, not a traditional QA tester. We're looking for the kind of person who set up SonarQube for an org, built quality gates into CI pipelines, created end-to-end testing frameworks, or stood up developer productivity metrics. This hire strengthens our "test" capability and ensures quality is embedded into the delivery pipeline by design.

Overview

We're looking for a DevOps/SRE Engineer with a quality engineering and developer productivity background to join Release Engineering. You'll own the tooling and infrastructure that ensures code quality, test reliability, and engineering productivity across all of our stacks, making it easy for engineers to do the right thing by default.

What You'll Do

• Own and evolve our code quality infrastructure: static analysis (SonarQube), quality gates, coverage reporting, and integration with CI/CD pipelines.
• Build and maintain testing frameworks and infrastructure: end-to-end testing platforms, test orchestration, flaky test detection, and test environment management.
• Define and implement quality gates across the delivery pipeline, from PR checks through staging and production promotion.
• Drive developer productivity tooling: build performance monitoring, CI feedback loops, and metrics dashboards (build times, test reliability, cycle time).
• Partner with engineering teams to create "golden paths" that embed quality into the standard development workflow without adding friction.

What We're Looking For

• 3+ years of experience in a platform, DevOps, SRE, or developer productivity role with a strong quality engineering focus.
• Hands-on experience setting up and operating code quality platforms (SonarQube, CodeClimate, or similar) and integrating them into CI/CD workflows.
• Experience building or maintaining test automation infrastructure (not writing test cases, but building the platforms that enable testing at scale).
• Familiarity with CI/CD systems, containerized environments, and build tooling.
• An engineering mindset: you solve quality problems with tooling and automation, not manual processes.

Nice to Have

• Experience with Bazel or monorepo build systems and their quality/testing integrations.
• Background in developer productivity metrics (DORA, SPACE, or custom frameworks).
• Familiarity with test impact analysis, selective test execution, or AI-assisted quality tooling.
• Experience working in a multi-stack environment (Go, JS/TS, Erlang/Elixir).

Why this role?

Our Erlang- and Elixir stack are critical parts of our platform, but the talent pool for engineers who understand the BEAM ecosystem and CI/CD/infrastructure is extremely small. This is a hard-to-fill but high-impact hire. The right person will own reliability and delivery for a stack that is central to our operations.

Overview

We're looking for a DevOps/SRE Engineer with hands-on experience in the Erlang and/or Elixir ecosystem to join Release Engineering. You'll own the build, test, and release pipelines for our BEAM-based services and ensure our delivery practices meet the same standard as the rest of the platform.

What You'll Do

• Own and evolve CI/CD pipelines for Erlang (Betler) and Elixir (Argo Workflows) services, from build through production deployment.
• Build and maintain base Docker images, release packaging, and deployment automation for BEAM-based applications.
• Collaborate with application teams to define and enforce release standards, quality gates, and rollback strategies for Erlang/Elixir services.
• Improve observability and reliability of the BEAM stack: build monitoring, deployment health checks, and incident response tooling.
• Contribute to migration efforts as we consolidate and modernize delivery infrastructure across stacks.

What We're Looking For

• 5+ years of experience operating, building, or deploying Erlang and/or Elixir applications in a production environment.
• Strong understanding of the BEAM VM, OTP patterns, hot code upgrades, and release management (rebar3, Mix releases).
• Hands-on experience with CI/CD systems (GitHub Actions, GitLab CI, Jenkins, Argo, or similar) and containerized deployments (Docker, Kubernetes).
• Infrastructure-as-code experience (Terraform, Helm, or similar).
• Comfortable working across the full delivery lifecycle, not just ops, not just dev.

Nice to Have

• Experience building or maintaining base images and build tooling for BEAM applications.
• Familiarity with multi-stack environments where Erlang/Elixir coexists with Go, JS/TS, or other runtimes.
• Background in high-availability domains (betting, gaming, fintech, telecom) where uptime directly impacts revenue.
• Experience with Argo Workflows or similar workflow orchestration platforms.

We're looking for a Senior Software Engineer to join our Application Frameworks team.

This is an exciting opportunity to help implement the common application development tool kits and frameworks (which internally we call Meta) that will be leveraged across all teams within Happening engineering. Your work will streamline building products, maintaining operational excellence, and meeting the needs of handling millions of interactions of end customers daily, built by hundreds of teams across the organization.

You will be working alongside talented engineers to create foundational libraries and tools that will set the standard for application development across the entire company.

What You'll Do

• Design and develop application frameworks and SDKs. Create reusable libraries, components, and tools that will empower engineering teams to build robust, scalable applications more efficiently.
• Implement best practices for Kubernetes optimization. Build applications that are optimized to run on k8s, taking into consideration state, restarts, continuous delivery, and GitOps principles.
• Create storage system integrations. Develop interfaces and abstractions for various storage technologies including relational databases, NoSQL, and event-driven or streaming architectures like PubSub.
• Drive technical standards. Collaborate with teams across the organization to establish and promote engineering best practices, coding standards, and architectural patterns.
• Support adoption and provide guidance. Help engineering teams successfully adopt our frameworks and tools through documentation, examples, and direct assistance.

What We're Looking For

• At least 5 years of software engineering experience with a focus on building maintainable, scalable systems.
• Strong proficiency in Go, Erlang, Elixir, or TypeScript with the capability and drive to learn new languages as needed.
• Experience building SDKs and Libraries ideally in a professional capacity or in open source projects with reasonable traction.
• Kubernetes expertise for building applications optimized to run on k8s, taking into consideration state, restarts, continuous delivery, and GitOps principles.
• Database and messaging system knowledge for building apps that leverage storage systems with relational databases, NoSQL, event-driven or streaming architectures like PubSub.
• Impact-driven mindset. You prioritize what's good for the business and supporting product engineers over working with trendy technologies.
• Commitment to simplicity. You strive for creating solutions that are simple to use and maintain, avoiding unnecessary complexity.
• Operational excellence focus. You're comfortable in an environment where operational excellence—from running software to adhering to team working cadences—is critical for sustainable success.
• Strong communication and collaboration skills. You can effectively work with diverse engineering teams and explain complex technical concepts to various stakeholders.

Overview

As a Staff Data Product Manager for the Analytics Platform, you will own the vision and roadmap for our core data foundation, primarily powered by Snowflake. Your mission is to bridge the "Perception vs. Reality" gap: while our platform is technically robust (with minimal incidents in 2025), users often find data hard to use or unclear.
You will define the "what" and "why" of our platform , ensuring it transitions from a reactive service to a strategic partner that empowers every team at Super to leverage data and AI capabilities at scale.

Key responsibilities

Product strategy & vision

• Own and articulate the product strategy and long-term vision for the Analytics Platform by defining the end-to-end analytical product lifecycle —- from data producer contract management and quality guardrails to downstream data consumers, their access patterns, and measurable value realisation.
• Develop a long-term vision for the Analytics Platform, aligning with broader data-driven objectives.
• Stay ahead of industry trends in cloud warehousing, data modeling, and emerging AI technologies.
• Propose and explore new ways of utilizing Snowflake to leverage AI capabilities and drive business value.

User research & advocacy

• Define and continuously evolve a clear measurement framework for Analytics Platform usability and adoption; use it to enable data-driven decisions with platform leaders and to directly shape roadmap priorities based on measurable impact.
• Deeply investigate actual customer needs, workflows, and pain points to eliminate "blind spots" in platform adoption.
• Address the gap between technical uptime and user perception by ensuring data is intuitive, clear, and reliable.
• Champion a user-centric culture, transforming how technical and business teams interact with our data models.

Roadmap & stakeholder management

• Build and maintain a prioritized product roadmap, balancing operational excellence with strategic initiatives.
• Collaborate with Product, Marketing, and Data Science to ensure Analytics platform is focused on most impactful initiatives across all areas

Strategic integration

• Partner with Product teams to ensure the Analytics Platform is a first-class citizen in the development lifecycle, not an afterthought.
• Partner with the Analytics Platform and Data leadership to transform high-level objectives into vision, direction and roadmap for a scalable, reliable technical platform.

Execution & delivery

• Translate business goals into well-defined problem statements, user stories, and acceptance criteria.
• Manage the development cycle in an Agile environment, providing clarity on priorities and monitoring progress.
• Ensure every initiative has a defined "Problem to Solve" and clear success measurement.

Qualifications

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, or a related field.
• 6–8+ years of product management experience, specifically with data products or analytics platforms.

2. Technical & domain expertise

• Strong familiarity with Analytics databases (e.g Snowflake, BigQuery, Redshift), ETL pipelines, DWH concepts and BI tools.
• Ability to convert complex technical concepts into clear business value.
• Proven track record in leading cross-functional teams

3. Strong differentiators

• Previous experience driving large-scale data product initiatives from ideation to launch
• Experience in entertainment, online gaming, or sports betting industries.

Overview

As a Staff Data Product Manager for the Analytics Platform, you will own the vision and roadmap for our core data foundation, primarily powered by Snowflake. Your mission is to bridge the "Perception vs. Reality" gap: while our platform is technically robust (with minimal incidents in 2025), users often find data hard to use or unclear.
You will define the "what" and "why" of our platform , ensuring it transitions from a reactive service to a strategic partner that empowers every team at Super to leverage data and AI capabilities at scale.

Key responsibilities

Product strategy & vision

• Own and articulate the product strategy and long-term vision for the Analytics Platform by defining the end-to-end analytical product lifecycle —- from data producer contract management and quality guardrails to downstream data consumers, their access patterns, and measurable value realisation.
• Develop a long-term vision for the Analytics Platform, aligning with broader data-driven objectives.
• Stay ahead of industry trends in cloud warehousing, data modeling, and emerging AI technologies.
• Propose and explore new ways of utilizing Snowflake to leverage AI capabilities and drive business value.

User research & advocacy

• Define and continuously evolve a clear measurement framework for Analytics Platform usability and adoption; use it to enable data-driven decisions with platform leaders and to directly shape roadmap priorities based on measurable impact.
• Deeply investigate actual customer needs, workflows, and pain points to eliminate "blind spots" in platform adoption.
• Address the gap between technical uptime and user perception by ensuring data is intuitive, clear, and reliable.
• Champion a user-centric culture, transforming how technical and business teams interact with our data models.

Roadmap & stakeholder management

• Build and maintain a prioritized product roadmap, balancing operational excellence with strategic initiatives.
• Collaborate with Product, Marketing, and Data Science to ensure Analytics platform is focused on most impactful initiatives across all areas

Strategic integration

• Partner with Product teams to ensure the Analytics Platform is a first-class citizen in the development lifecycle, not an afterthought.
• Partner with the Analytics Platform and Data leadership to transform high-level objectives into vision, direction and roadmap for a scalable, reliable technical platform.

Execution & delivery

• Translate business goals into well-defined problem statements, user stories, and acceptance criteria.
• Manage the development cycle in an Agile environment, providing clarity on priorities and monitoring progress.
• Ensure every initiative has a defined "Problem to Solve" and clear success measurement.

Qualifications

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, or a related field.
• 6–8+ years of product management experience, specifically with data products or analytics platforms.

2. Technical & domain expertise

• Strong familiarity with Analytics databases (e.g Snowflake, BigQuery, Redshift), ETL pipelines, DWH concepts and BI tools.
• Ability to convert complex technical concepts into clear business value.
• Proven track record in leading cross-functional teams

3. Strong differentiators

• Previous experience driving large-scale data product initiatives from ideation to launch
• Experience in entertainment, online gaming, or sports betting industries.

Overview

As a Staff Data Product Manager for the Analytics Platform, you will own the vision and roadmap for our core data foundation, primarily powered by Snowflake. Your mission is to bridge the "Perception vs. Reality" gap: while our platform is technically robust (with minimal incidents in 2025), users often find data hard to use or unclear.
You will define the "what" and "why" of our platform , ensuring it transitions from a reactive service to a strategic partner that empowers every team at Super to leverage data and AI capabilities at scale.

Key responsibilities

Product strategy & vision

• Own and articulate the product strategy and long-term vision for the Analytics Platform by defining the end-to-end analytical product lifecycle —- from data producer contract management and quality guardrails to downstream data consumers, their access patterns, and measurable value realisation.
• Develop a long-term vision for the Analytics Platform, aligning with broader data-driven objectives.
• Stay ahead of industry trends in cloud warehousing, data modeling, and emerging AI technologies.
• Propose and explore new ways of utilizing Snowflake to leverage AI capabilities and drive business value.

User research & advocacy

• Define and continuously evolve a clear measurement framework for Analytics Platform usability and adoption; use it to enable data-driven decisions with platform leaders and to directly shape roadmap priorities based on measurable impact.
• Deeply investigate actual customer needs, workflows, and pain points to eliminate "blind spots" in platform adoption.
• Address the gap between technical uptime and user perception by ensuring data is intuitive, clear, and reliable.
• Champion a user-centric culture, transforming how technical and business teams interact with our data models.

Roadmap & stakeholder management

• Build and maintain a prioritized product roadmap, balancing operational excellence with strategic initiatives.
• Collaborate with Product, Marketing, and Data Science to ensure Analytics platform is focused on most impactful initiatives across all areas

Strategic integration

• Partner with Product teams to ensure the Analytics Platform is a first-class citizen in the development lifecycle, not an afterthought.
• Partner with the Analytics Platform and Data leadership to transform high-level objectives into vision, direction and roadmap for a scalable, reliable technical platform.

Execution & delivery

• Translate business goals into well-defined problem statements, user stories, and acceptance criteria.
• Manage the development cycle in an Agile environment, providing clarity on priorities and monitoring progress.
• Ensure every initiative has a defined "Problem to Solve" and clear success measurement.

Qualifications

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, or a related field.
• 6–8+ years of product management experience, specifically with data products or analytics platforms.

2. Technical & domain expertise

• Strong familiarity with Analytics databases (e.g Snowflake, BigQuery, Redshift), ETL pipelines, DWH concepts and BI tools.
• Ability to convert complex technical concepts into clear business value.
• Proven track record in leading cross-functional teams

3. Strong differentiators

• Previous experience driving large-scale data product initiatives from ideation to launch
• Experience in entertainment, online gaming, or sports betting industries.

Overview

As a Staff Data Product Manager for the Analytics Platform, you will own the vision and roadmap for our core data foundation, primarily powered by Snowflake. Your mission is to bridge the "Perception vs. Reality" gap: while our platform is technically robust (with minimal incidents in 2025), users often find data hard to use or unclear.
You will define the "what" and "why" of our platform , ensuring it transitions from a reactive service to a strategic partner that empowers every team at Super to leverage data and AI capabilities at scale.

Key responsibilities

Product strategy & vision

• Own and articulate the product strategy and long-term vision for the Analytics Platform by defining the end-to-end analytical product lifecycle —- from data producer contract management and quality guardrails to downstream data consumers, their access patterns, and measurable value realisation.
• Develop a long-term vision for the Analytics Platform, aligning with broader data-driven objectives.
• Stay ahead of industry trends in cloud warehousing, data modeling, and emerging AI technologies.
• Propose and explore new ways of utilizing Snowflake to leverage AI capabilities and drive business value.

User research & advocacy

• Define and continuously evolve a clear measurement framework for Analytics Platform usability and adoption; use it to enable data-driven decisions with platform leaders and to directly shape roadmap priorities based on measurable impact.
• Deeply investigate actual customer needs, workflows, and pain points to eliminate "blind spots" in platform adoption.
• Address the gap between technical uptime and user perception by ensuring data is intuitive, clear, and reliable.
• Champion a user-centric culture, transforming how technical and business teams interact with our data models.

Roadmap & stakeholder management

• Build and maintain a prioritized product roadmap, balancing operational excellence with strategic initiatives.
• Collaborate with Product, Marketing, and Data Science to ensure Analytics platform is focused on most impactful initiatives across all areas

Strategic integration

• Partner with Product teams to ensure the Analytics Platform is a first-class citizen in the development lifecycle, not an afterthought.
• Partner with the Analytics Platform and Data leadership to transform high-level objectives into vision, direction and roadmap for a scalable, reliable technical platform.

Execution & delivery

• Translate business goals into well-defined problem statements, user stories, and acceptance criteria.
• Manage the development cycle in an Agile environment, providing clarity on priorities and monitoring progress.
• Ensure every initiative has a defined "Problem to Solve" and clear success measurement.

Qualifications

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, or a related field.
• 6–8+ years of product management experience, specifically with data products or analytics platforms.

2. Technical & domain expertise

• Strong familiarity with Analytics databases (e.g Snowflake, BigQuery, Redshift), ETL pipelines, DWH concepts and BI tools.
• Ability to convert complex technical concepts into clear business value.
• Proven track record in leading cross-functional teams

3. Strong differentiators

• Previous experience driving large-scale data product initiatives from ideation to launch
• Experience in entertainment, online gaming, or sports betting industries.

Overview

As a Senior Data Product Manager – Experimentation, you will own the vision, strategy, and roadmap of our internal experimentation platform. Your primary focus will be defining “what” we build and “why”—ensuring our teams have robust, intuitive tools for A/B testing, audience segmentation, feature flag management, and data-driven decision-making. Through close collaboration with Engineering, Data Science, Marketing, and Product teams, you will champion a hypothesis-driven culture, transforming how we learn, iterate, and innovate at scale. Your work will empower every team at SuperBet to rapidly experiment, reduce time-to-insight, and drive measurable business impact.

Key responsibilities

1. Product strategy & vision

• Develop a clear, long-term product vision for the Experimentation Platform, aligning with the broader data-driven objectives.
• Stay attuned to industry best practices, academic research, and emerging technologies to keep our platform at the cutting edge.

2. Defining the “What” and “Why”

• Translate business goals and user requirements into well-defined problem statements, expected business impact, and success metrics.
• Advocate for the importance of hypothesis-driven development, evangelizing experimentation methodologies across the organization.

3. Roadmap & stakeholder management

• Build and maintain a prioritized product roadmap for experimentation, balancing quick wins with strategic, long-term initiatives.
• Collaborate with cross-functional stakeholders (Product, Marketing, Data Science, Engineering) to gather feedback, refine requirements, and drive alignment.

4. Cross-functional collaboration

• Partner closely with the Senior Engineering Manager to transform high-level product objectives into a scalable, reliable, and efficient technical platform.
• Collaborate with Data Scientists and Analysts to define statistical engines, experiment design templates, and consistent KPI frameworks.
• Work with Marketing and Product teams to ensure the experimentation platform meets diverse needs—ranging from audience segmentation to feature flagging and surveys.

5. Execution & delivery

• Clearly communicate product requirements, user stories, and acceptance criteria to engineering teams.
• Manage the development cycle in an Agile environment, providing clarity on priorities and monitoring progress.
• Ensure each initiative launches with a defined Problem to Solve, Business Impact, and Success Measurement, tracking progress to inform continuous improvement.

6. Performance & impact tracking

• Define robust product success metrics (adoption rates, number of experiments run, time-to-insight, etc.) and implement tracking mechanisms.
• Analyze usage data and experiment results to validate assumptions, identify gaps, and inform product enhancements.
• Champion experimentation quality measurements—ensuring teams follow best practices and extract meaningful insights.
• Work with user communities to collect feedback, refine feature requirements, and iterate on platform improvements.

7. Domain expertise & thought leadership

• Serve as the in-house expert on experimentation methodologies, statistical significance, confidence intervals, and advanced testing frameworks.
• Lead initiatives like an Experimentation Embassy, workshops, and knowledge-sharing sessions to promote best practices across the company.

8. Communication & evangelism

• Own the narrative around experimentation—communicating platform updates, success stories, and impact to both technical and non-technical audiences.
• Continuously evangelize the power of a hypothesis-driven, data-informed approach within the organisation, aligning executives and teams on key objectives.

Qualifications

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, Statistics, or a related field (or equivalent practical experience).
• 6–8+ years of product management experience, ideally with a focus on data products, analytics, or experimentation platforms.

2. Technical & domain expertise

• Strong familiarity with A/B testing, multivariate testing, feature flag management, and audience segmentation.
• Understanding of statistical methods (confidence intervals, p-values, hypothesis testing) and ability to collaborate with data scientists on experiment designs.
• Exposure to data infrastructure, ETL pipelines, BI tools, and relevant technologies.

3. Product management skills

• Proven track record in leading cross-functional teams (Engineering, Data Science, UX) using Agile methodologies.
• Ability to convert complex data or statistical concepts into clear business value and actionable product requirements.
• Skilled in developing roadmaps, facilitating stakeholder alignment, and prioritizing high-impact features.

4. Strong differentiators

• Hands-on experience building experimentation platforms, customer segmentation systems, or ML evaluation frameworks.
• Previous work in entertainment, online gaming, or sports betting industries, especially around experimentation and user experience improvements.
• Previous experience driving large-scale data product initiatives, from ideation to launch.

Overview

As a Senior Data Product Manager – Experimentation, you will own the vision, strategy, and roadmap of our internal experimentation platform. Your primary focus will be defining “what” we build and “why”—ensuring our teams have robust, intuitive tools for A/B testing, audience segmentation, feature flag management, and data-driven decision-making. Through close collaboration with Engineering, Data Science, Marketing, and Product teams, you will champion a hypothesis-driven culture, transforming how we learn, iterate, and innovate at scale. Your work will empower every team at SuperBet to rapidly experiment, reduce time-to-insight, and drive measurable business impact.

Key responsibilities

1. Product strategy & vision

• Develop a clear, long-term product vision for the Experimentation Platform, aligning with the broader data-driven objectives.
• Stay attuned to industry best practices, academic research, and emerging technologies to keep our platform at the cutting edge.

2. Defining the “What” and “Why”

• Translate business goals and user requirements into well-defined problem statements, expected business impact, and success metrics.
• Advocate for the importance of hypothesis-driven development, evangelizing experimentation methodologies across the organization.

3. Roadmap & stakeholder management

• Build and maintain a prioritized product roadmap for experimentation, balancing quick wins with strategic, long-term initiatives.
• Collaborate with cross-functional stakeholders (Product, Marketing, Data Science, Engineering) to gather feedback, refine requirements, and drive alignment.

4. Cross-functional collaboration

• Partner closely with the Senior Engineering Manager to transform high-level product objectives into a scalable, reliable, and efficient technical platform.
• Collaborate with Data Scientists and Analysts to define statistical engines, experiment design templates, and consistent KPI frameworks.
• Work with Marketing and Product teams to ensure the experimentation platform meets diverse needs—ranging from audience segmentation to feature flagging and surveys.

5. Execution & delivery

• Clearly communicate product requirements, user stories, and acceptance criteria to engineering teams.
• Manage the development cycle in an Agile environment, providing clarity on priorities and monitoring progress.
• Ensure each initiative launches with a defined Problem to Solve, Business Impact, and Success Measurement, tracking progress to inform continuous improvement.

6. Performance & impact tracking

• Define robust product success metrics (adoption rates, number of experiments run, time-to-insight, etc.) and implement tracking mechanisms.
• Analyze usage data and experiment results to validate assumptions, identify gaps, and inform product enhancements.
• Champion experimentation quality measurements—ensuring teams follow best practices and extract meaningful insights.
• Work with user communities to collect feedback, refine feature requirements, and iterate on platform improvements.

7. Domain expertise & thought leadership

• Serve as the in-house expert on experimentation methodologies, statistical significance, confidence intervals, and advanced testing frameworks.
• Lead initiatives like an Experimentation Embassy, workshops, and knowledge-sharing sessions to promote best practices across the company.

8. Communication & evangelism

• Own the narrative around experimentation—communicating platform updates, success stories, and impact to both technical and non-technical audiences.
• Continuously evangelize the power of a hypothesis-driven, data-informed approach within the organisation, aligning executives and teams on key objectives.

Qualifications

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, Statistics, or a related field (or equivalent practical experience).
• 6–8+ years of product management experience, ideally with a focus on data products, analytics, or experimentation platforms.

2. Technical & domain expertise

• Strong familiarity with A/B testing, multivariate testing, feature flag management, and audience segmentation.
• Understanding of statistical methods (confidence intervals, p-values, hypothesis testing) and ability to collaborate with data scientists on experiment designs.
• Exposure to data infrastructure, ETL pipelines, BI tools, and relevant technologies.

3. Product management skills

• Proven track record in leading cross-functional teams (Engineering, Data Science, UX) using Agile methodologies.
• Ability to convert complex data or statistical concepts into clear business value and actionable product requirements.
• Skilled in developing roadmaps, facilitating stakeholder alignment, and prioritizing high-impact features.

4. Strong differentiators

• Hands-on experience building experimentation platforms, customer segmentation systems, or ML evaluation frameworks.
• Previous work in entertainment, online gaming, or sports betting industries, especially around experimentation and user experience improvements.
• Previous experience driving large-scale data product initiatives, from ideation to launch.

Overview

As a Senior Data Product Manager – Experimentation, you will own the vision, strategy, and roadmap of our internal experimentation platform. Your primary focus will be defining “what” we build and “why”—ensuring our teams have robust, intuitive tools for A/B testing, audience segmentation, feature flag management, and data-driven decision-making. Through close collaboration with Engineering, Data Science, Marketing, and Product teams, you will champion a hypothesis-driven culture, transforming how we learn, iterate, and innovate at scale. Your work will empower every team at SuperBet to rapidly experiment, reduce time-to-insight, and drive measurable business impact.

Key responsibilities

1. Product strategy & vision

• Develop a clear, long-term product vision for the Experimentation Platform, aligning with the broader data-driven objectives.
• Stay attuned to industry best practices, academic research, and emerging technologies to keep our platform at the cutting edge.

2. Defining the “What” and “Why”

• Translate business goals and user requirements into well-defined problem statements, expected business impact, and success metrics.
• Advocate for the importance of hypothesis-driven development, evangelizing experimentation methodologies across the organization.

3. Roadmap & stakeholder management

• Build and maintain a prioritized product roadmap for experimentation, balancing quick wins with strategic, long-term initiatives.
• Collaborate with cross-functional stakeholders (Product, Marketing, Data Science, Engineering) to gather feedback, refine requirements, and drive alignment.

4. Cross-functional collaboration

• Partner closely with the Senior Engineering Manager to transform high-level product objectives into a scalable, reliable, and efficient technical platform.
• Collaborate with Data Scientists and Analysts to define statistical engines, experiment design templates, and consistent KPI frameworks.
• Work with Marketing and Product teams to ensure the experimentation platform meets diverse needs—ranging from audience segmentation to feature flagging and surveys.

5. Execution & delivery

• Clearly communicate product requirements, user stories, and acceptance criteria to engineering teams.
• Manage the development cycle in an Agile environment, providing clarity on priorities and monitoring progress.
• Ensure each initiative launches with a defined Problem to Solve, Business Impact, and Success Measurement, tracking progress to inform continuous improvement.

6. Performance & impact tracking

• Define robust product success metrics (adoption rates, number of experiments run, time-to-insight, etc.) and implement tracking mechanisms.
• Analyze usage data and experiment results to validate assumptions, identify gaps, and inform product enhancements.
• Champion experimentation quality measurements—ensuring teams follow best practices and extract meaningful insights.
• Work with user communities to collect feedback, refine feature requirements, and iterate on platform improvements.

7. Domain expertise & thought leadership

• Serve as the in-house expert on experimentation methodologies, statistical significance, confidence intervals, and advanced testing frameworks.
• Lead initiatives like an Experimentation Embassy, workshops, and knowledge-sharing sessions to promote best practices across the company.

8. Communication & evangelism

• Own the narrative around experimentation—communicating platform updates, success stories, and impact to both technical and non-technical audiences.
• Continuously evangelize the power of a hypothesis-driven, data-informed approach within the organisation, aligning executives and teams on key objectives.

Qualifications

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, Statistics, or a related field (or equivalent practical experience).
• 6–8+ years of product management experience, ideally with a focus on data products, analytics, or experimentation platforms.

2. Technical & domain expertise

• Strong familiarity with A/B testing, multivariate testing, feature flag management, and audience segmentation.
• Understanding of statistical methods (confidence intervals, p-values, hypothesis testing) and ability to collaborate with data scientists on experiment designs.
• Exposure to data infrastructure, ETL pipelines, BI tools, and relevant technologies.

3. Product management skills

• Proven track record in leading cross-functional teams (Engineering, Data Science, UX) using Agile methodologies.
• Ability to convert complex data or statistical concepts into clear business value and actionable product requirements.
• Skilled in developing roadmaps, facilitating stakeholder alignment, and prioritizing high-impact features.

4. Strong differentiators

• Hands-on experience building experimentation platforms, customer segmentation systems, or ML evaluation frameworks.
• Previous work in entertainment, online gaming, or sports betting industries, especially around experimentation and user experience improvements.
• Previous experience driving large-scale data product initiatives, from ideation to launch.

Overview

As a Senior Data Product Manager – Experimentation, you will own the vision, strategy, and roadmap of our internal experimentation platform. Your primary focus will be defining “what” we build and “why”—ensuring our teams have robust, intuitive tools for A/B testing, audience segmentation, feature flag management, and data-driven decision-making. Through close collaboration with Engineering, Data Science, Marketing, and Product teams, you will champion a hypothesis-driven culture, transforming how we learn, iterate, and innovate at scale. Your work will empower every team at SuperBet to rapidly experiment, reduce time-to-insight, and drive measurable business impact.

Key responsibilities

1. Product strategy & vision

• Develop a clear, long-term product vision for the Experimentation Platform, aligning with the broader data-driven objectives.
• Stay attuned to industry best practices, academic research, and emerging technologies to keep our platform at the cutting edge.

2. Defining the “What” and “Why”

• Translate business goals and user requirements into well-defined problem statements, expected business impact, and success metrics.
• Advocate for the importance of hypothesis-driven development, evangelizing experimentation methodologies across the organization.

3. Roadmap & stakeholder management

• Build and maintain a prioritized product roadmap for experimentation, balancing quick wins with strategic, long-term initiatives.
• Collaborate with cross-functional stakeholders (Product, Marketing, Data Science, Engineering) to gather feedback, refine requirements, and drive alignment.

4. Cross-functional collaboration

• Partner closely with the Senior Engineering Manager to transform high-level product objectives into a scalable, reliable, and efficient technical platform.
• Collaborate with Data Scientists and Analysts to define statistical engines, experiment design templates, and consistent KPI frameworks.
• Work with Marketing and Product teams to ensure the experimentation platform meets diverse needs—ranging from audience segmentation to feature flagging and surveys.

5. Execution & delivery

• Clearly communicate product requirements, user stories, and acceptance criteria to engineering teams.
• Manage the development cycle in an Agile environment, providing clarity on priorities and monitoring progress.
• Ensure each initiative launches with a defined Problem to Solve, Business Impact, and Success Measurement, tracking progress to inform continuous improvement.

6. Performance & impact tracking

• Define robust product success metrics (adoption rates, number of experiments run, time-to-insight, etc.) and implement tracking mechanisms.
• Analyze usage data and experiment results to validate assumptions, identify gaps, and inform product enhancements.
• Champion experimentation quality measurements—ensuring teams follow best practices and extract meaningful insights.
• Work with user communities to collect feedback, refine feature requirements, and iterate on platform improvements.

7. Domain expertise & thought leadership

• Serve as the in-house expert on experimentation methodologies, statistical significance, confidence intervals, and advanced testing frameworks.
• Lead initiatives like an Experimentation Embassy, workshops, and knowledge-sharing sessions to promote best practices across the company.

8. Communication & evangelism

• Own the narrative around experimentation—communicating platform updates, success stories, and impact to both technical and non-technical audiences.
• Continuously evangelize the power of a hypothesis-driven, data-informed approach within the organisation, aligning executives and teams on key objectives.

Qualifications

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, Statistics, or a related field (or equivalent practical experience).
• 6–8+ years of product management experience, ideally with a focus on data products, analytics, or experimentation platforms.

2. Technical & domain expertise

• Strong familiarity with A/B testing, multivariate testing, feature flag management, and audience segmentation.
• Understanding of statistical methods (confidence intervals, p-values, hypothesis testing) and ability to collaborate with data scientists on experiment designs.
• Exposure to data infrastructure, ETL pipelines, BI tools, and relevant technologies.

3. Product management skills

• Proven track record in leading cross-functional teams (Engineering, Data Science, UX) using Agile methodologies.
• Ability to convert complex data or statistical concepts into clear business value and actionable product requirements.
• Skilled in developing roadmaps, facilitating stakeholder alignment, and prioritizing high-impact features.

4. Strong differentiators

• Hands-on experience building experimentation platforms, customer segmentation systems, or ML evaluation frameworks.
• Previous work in entertainment, online gaming, or sports betting industries, especially around experimentation and user experience improvements.
• Previous experience driving large-scale data product initiatives, from ideation to launch.

Overview

As a Senior Data Product Manager – Experimentation, you will own the vision, strategy, and roadmap of our internal experimentation platform. Your primary focus will be defining “what” we build and “why”—ensuring our teams have robust, intuitive tools for A/B testing, audience segmentation, feature flag management, and data-driven decision-making. Through close collaboration with Engineering, Data Science, Marketing, and Product teams, you will champion a hypothesis-driven culture, transforming how we learn, iterate, and innovate at scale. Your work will empower every team at SuperBet to rapidly experiment, reduce time-to-insight, and drive measurable business impact.

Key responsibilities

1. Product strategy & vision

• Develop a clear, long-term product vision for the Experimentation Platform, aligning with the broader data-driven objectives.
• Stay attuned to industry best practices, academic research, and emerging technologies to keep our platform at the cutting edge.

2. Defining the “What” and “Why”

• Translate business goals and user requirements into well-defined problem statements, expected business impact, and success metrics.
• Advocate for the importance of hypothesis-driven development, evangelizing experimentation methodologies across the organization.

3. Roadmap & stakeholder management

• Build and maintain a prioritized product roadmap for experimentation, balancing quick wins with strategic, long-term initiatives.
• Collaborate with cross-functional stakeholders (Product, Marketing, Data Science, Engineering) to gather feedback, refine requirements, and drive alignment.

4. Cross-functional collaboration

• Partner closely with the Senior Engineering Manager to transform high-level product objectives into a scalable, reliable, and efficient technical platform.
• Collaborate with Data Scientists and Analysts to define statistical engines, experiment design templates, and consistent KPI frameworks.
• Work with Marketing and Product teams to ensure the experimentation platform meets diverse needs—ranging from audience segmentation to feature flagging and surveys.

5. Execution & delivery

• Clearly communicate product requirements, user stories, and acceptance criteria to engineering teams.
• Manage the development cycle in an Agile environment, providing clarity on priorities and monitoring progress.
• Ensure each initiative launches with a defined Problem to Solve, Business Impact, and Success Measurement, tracking progress to inform continuous improvement.

6. Performance & impact tracking

• Define robust product success metrics (adoption rates, number of experiments run, time-to-insight, etc.) and implement tracking mechanisms.
• Analyze usage data and experiment results to validate assumptions, identify gaps, and inform product enhancements.
• Champion experimentation quality measurements—ensuring teams follow best practices and extract meaningful insights.
• Work with user communities to collect feedback, refine feature requirements, and iterate on platform improvements.

7. Domain expertise & thought leadership

• Serve as the in-house expert on experimentation methodologies, statistical significance, confidence intervals, and advanced testing frameworks.
• Lead initiatives like an Experimentation Embassy, workshops, and knowledge-sharing sessions to promote best practices across the company.

8. Communication & evangelism

• Own the narrative around experimentation—communicating platform updates, success stories, and impact to both technical and non-technical audiences.
• Continuously evangelize the power of a hypothesis-driven, data-informed approach within the organisation, aligning executives and teams on key objectives.

Qualifications

1. Education & experience

• Bachelor’s or Master’s degree in Business, Computer Science, Data Science, Statistics, or a related field (or equivalent practical experience).
• 6–8+ years of product management experience, ideally with a focus on data products, analytics, or experimentation platforms.

2. Technical & domain expertise

• Strong familiarity with A/B testing, multivariate testing, feature flag management, and audience segmentation.
• Understanding of statistical methods (confidence intervals, p-values, hypothesis testing) and ability to collaborate with data scientists on experiment designs.
• Exposure to data infrastructure, ETL pipelines, BI tools, and relevant technologies.

3. Product management skills

• Proven track record in leading cross-functional teams (Engineering, Data Science, UX) using Agile methodologies.
• Ability to convert complex data or statistical concepts into clear business value and actionable product requirements.
• Skilled in developing roadmaps, facilitating stakeholder alignment, and prioritizing high-impact features.

4. Strong differentiators

• Hands-on experience building experimentation platforms, customer segmentation systems, or ML evaluation frameworks.
• Previous work in entertainment, online gaming, or sports betting industries, especially around experimentation and user experience improvements.
• Previous experience driving large-scale data product initiatives, from ideation to launch.

We're looking for a Senior Software Engineer to join our Application Frameworks team.

This is an exciting opportunity to help implement the common application development tool kits and frameworks (which internally we call Meta) that will be leveraged across all teams within Happening engineering. Your work will streamline building products, maintaining operational excellence, and meeting the needs of handling millions of interactions of end customers daily, built by hundreds of teams across the organization.

You will be working alongside talented engineers to create foundational libraries and tools that will set the standard for application development across the entire company.

What You'll Do

• Design and develop application frameworks and SDKs. Create reusable libraries, components, and tools that will empower engineering teams to build robust, scalable applications more efficiently.
• Implement best practices for Kubernetes optimization. Build applications that are optimized to run on k8s, taking into consideration state, restarts, continuous delivery, and GitOps principles.
• Create storage system integrations. Develop interfaces and abstractions for various storage technologies including relational databases, NoSQL, and event-driven or streaming architectures like PubSub.
• Drive technical standards. Collaborate with teams across the organization to establish and promote engineering best practices, coding standards, and architectural patterns.
• Support adoption and provide guidance. Help engineering teams successfully adopt our frameworks and tools through documentation, examples, and direct assistance.

What We're Looking For

• At least 5 years of software engineering experience with a focus on building maintainable, scalable systems.
• Strong proficiency in Go, Erlang, Elixir, or TypeScript with the capability and drive to learn new languages as needed.
• Experience building SDKs and Libraries ideally in a professional capacity or in open source projects with reasonable traction.
• Kubernetes expertise for building applications optimized to run on k8s, taking into consideration state, restarts, continuous delivery, and GitOps principles.
• Database and messaging system knowledge for building apps that leverage storage systems with relational databases, NoSQL, event-driven or streaming architectures like PubSub.
• Impact-driven mindset. You prioritize what's good for the business and supporting product engineers over working with trendy technologies.
• Commitment to simplicity. You strive for creating solutions that are simple to use and maintain, avoiding unnecessary complexity.
• Operational excellence focus. You're comfortable in an environment where operational excellence—from running software to adhering to team working cadences—is critical for sustainable success.
• Strong communication and collaboration skills. You can effectively work with diverse engineering teams and explain complex technical concepts to various stakeholders.

We're looking for a Senior Data Platform Engineer to join our Storage Systems Tribe in the Messaging Squad.

This is an exciting opportunity for an experienced engineer to make a significant impact on our messaging architecture. In this role, you will be responsible for designing, building, and scaling our Kafka infrastructure to support hundreds of millions of messages a day. Your work will directly enable hundreds of engineers to build high-volume products that meet the demands of both customer-facing applications and internal data and analytics engineering use cases.

You will be working alongside engineering colleagues to create a robust messaging platform that serves as a critical foundation for our business operations and growth.

What You'll Do

• Design and scale our Kafka architecture. Build and maintain a high-performance messaging infrastructure capable of handling hundreds of millions of daily messages with reliability and efficiency.
• Develop SDKs and abstractions. Create intuitive interfaces that enable product and feature teams to easily integrate with our messaging platform without needing to understand the underlying complexity.
• Implement operational excellence. Establish monitoring, alerting, and performance optimization practices to ensure system reliability and scalability.
• Collaborate across teams. Work closely with platform framework teams to build common tools and abstractions that streamline usage and enhance monitoring capabilities.
• Drive architectural decisions. Make key technical decisions related to storage system provisioning, tuning, and scaling to support our growing business needs.
• Support data and analytics workflows. Ensure our messaging infrastructure effectively serves internal use cases related to data engineering and analytics.

What We're Looking For

• 5+ years of experience working with storage system provisioning, tuning, and scaling. Deep knowledge of distributed systems principles and their practical application.
• Proven experience with Kafka. Strong understanding of Kafka architecture, including brokers, topics, partitions, consumer groups, and replication. Experience with NATs, ActiveMQ, or related technologies is also acceptable.
• Experience developing developer experience capabilities. Track record of creating interfaces, SDKs, and abstractions that make complex systems accessible to product and feature teams.
• Proficiency with cloud-native technologies. Experience with Kubernetes, containerization, and cloud environments (AWS, GCP, or Azure).
• Solid programming skills. Proficiency in Java, Scala, Go, or other languages commonly used in distributed systems development.
• Knowledge of data integration patterns. Understanding of CDC (Change Data Capture), ETL/ELT workflows, and event-driven architectures.
• Impact-driven mindset. You prioritize what's good for the business and supporting product engineers over working with trendy technologies.
• Commitment to simplicity. You strive for creating solutions that are simple to use and maintain, avoiding unnecessary complexity.
• Excellent communication abilities. You can effectively collaborate with diverse engineering teams and explain complex technical concepts to various stakeholders.

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects.

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects.

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito)
• Exposure to streaming data platforms like Kafka
• Contributions to open-source ML projects

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects.

As a Senior Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule-based components)
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability
• Communicate model behavior, trade-offs, and results clearly to both technical and non-technical stakeholders
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices

We're looking for someone with:

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus)
• 4+ years of industry experience building and deploying ML systems
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices
• Strong problem-solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

We’re looking for a Product Manager, Technical (PMT) to join our Platform Reliability area.

This is an opportunity to own the product vision and strategy for the tooling, platforms, and practices that keep Super reliable while dramatically improving developer productivity. You’ll work at the intersection of observability, incident management, and developer experience, making Super Technologies engineers your primary customer so they can spend more time shipping value and less time firefighting.

Overview of the Role

The Product Manager-Technical (PMT) role is characterized by a strategic blend of product vision and technical expertise. This individual is responsible for driving product development from conception through launch and beyond. The PMT leverages data-driven insights to define product strategy and roadmaps, ensuring alignment with business objectives. They collaborate closely with engineering teams, translating customer needs into detailed technical requirements and specifications. A critical aspect of this role involves fostering cross-functional collaboration, effectively communicating with stakeholders, and influencing without direct authority. The PMT is expected to demonstrate a strong understanding of software development principles, analyze complex data sets, and drive decision-making based on measurable outcomes. End-to-end ownership is paramount, requiring the PMT to proactively identify and address challenges, manage product backlogs, and continuously optimize product performance.

In Release, this means owning the software delivery lifecycle from development- to production, and the developer experience within that scope. Your customers are primarily Super Technologies engineers, and your goal is to amplify their impact by giving them clear signals, powerful tools, and simple paths to developing-, building-, testing and releasing applications.

What You’ll Do

• Own the product vision, strategy, and roadmap for Release engineering, spanning development, build, testing, and releasing used by engineering teams across Super.
• Treat Super Technologies engineers as your primary customers: deeply understand their workflows, pain points, and cognitive load when building and operating services, and translate these into clear product requirements.
• Define and evolve the complete developer experience around shipping our applications reliably the right way.
• Partner with engineering teams to understand the key user- journeys and points of friction to create a cohesive and excellent developer platform.
• Drive adoption of platform best practices, and converge on unified contracts across capabilities.
• Use data to guide decisions: define and track KPIs such as MTTR, cycle time, release frequency, signal-to-noise ratio, and developer productivity metrics related to our SDLC.
• Collaborate closely with Platform, Product, Security, and vertical teams to sequence the roadmap, balance short-term firefighting with long-term investments, and land changes through effective enablement and change management.
• Maintain and prioritize a transparent backlog across multiple stakeholder groups, ensuring clarity of trade-offs, timelines, and success criteria.
• Work with engineering counterparts to align on technical architecture, integration points, and rollout plans for reliability tooling and observability platforms.
• Champion a culture of continuous improvement, helping teams move in a single direction and increase the impact of the most recent LLM motions.

What We’re Looking For

• 5+ years of experience in product management or technical program/product roles, with at least part of that time focused on platform, infrastructure, SRE, or developer experience products.
• Strong background software delivery lifecycle, CI/CD pipelines and application runtimes. experience balancing the needs from several ecosystems whilst maintaining focus.
• Hands-on experience with CI/CD systems, Kubernetes and software development.
• Proven ability to work with engineering teams on technical problems: you are comfortable discussing architectures, failure modes, SLOs, and operational constraints, and can translate these into product decisions and requirements.
• Demonstrated success building products or platforms for internal engineering customers, with measurable impact on developer productivity, reliability, or delivery speed.
• Ability to define, track, and communicate outcome-oriented metrics (e.g., Build times, release failure rate, release frequency, developer satisfaction, product adoption) and use them to drive prioritization and continuous improvement.
• Excellent communication and stakeholder management skills, with a track record of influencing without formal authority across multiple teams and seniority levels.
• Strong analytical and problem-solving skills, including comfort working with data to validate hypotheses, evaluate trade-offs, and measure impact.
• A mindset of end-to-end ownership: from discovery and problem definition through delivery, rollout, adoption, and iteration.

Nice to Have

• Direct experience working with SRE, DevOps, or platform teams in a high-scale, multi-service environment.
• Experience designing or CI/CD tooling (for example, migrating complex workloads across systems).
• Familiarity with developer productivity tooling and workflows: Deature flags, deployment strategies, service catalogues, and runbook automation.
• Experience connecting engineering signals to customer experience and business metrics, such as customer journey SLOs, error budgets, or funnel drop-offs.
• Experience introducing or evolving “golden paths” and self-service platforms that reduce cognitive load for engineers and make it easy to do the right thing by default.
• Comfort working with AI- and automation-based approaches to reliability and DevEx (for example: Code reviews, Quality gates, automated diagnostics, or developer copilots for operational tasks).
• Background in high-availability, high-reliability domains (e.g., fintech, betting, gaming, large-scale consumer platforms) where uptime and performance directly impact revenue and customer trust.