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.

Staff Software Engineer — Data Platform (Europe)

This is a rare role for an engineer who's genuinely comfortable on both sides of the stack — reading Java Spring Boot services in the morning and writing PySpark pipelines in the afternoon. You'll own the end-to-end data flow that sits behind every pricing decision Duetto makes for thousands of hotels worldwide, bridging our Java core platform and Python data layer while working in an engineering culture where AI is already how the work gets done.

What Makes Us Different?

Duetto is the hospitality industry's leading revenue management platform, founded in 2012 by former Wynn Resorts executives who knew the industry needed better technology. We built the world's first Revenue & Profit Operating System — a suite of tools (GameChanger, ScoreBoard, BlockBuster, Advance and more) that goes beyond room pricing to give hotels, resorts and casinos a complete picture of their revenue and profitability. Trusted by clients ranging from independent boutique hotels to global chains, we've been named the #1 Revenue Management Software by HotelTechAwards four years running and the #1 Best Place to Work in Hotel Tech in 2025. Backed by GrowthCurve Capital since 2024, we're accelerating our investment in AI — and we're genuinely passionate about the industry we serve. We build products we're proud of, for customers we care about.

What You'll Be Doing

• You'll own data pipelines from source to gold — taking data from MongoDB, Kinesis events, RabbitMQ, and PMS/CRS integrations through the full bronze → silver → gold lakehouse architecture, including Iceberg-based ingestion patterns for both batch and near-real-time workloads.
• You'll build connectors and transformations for new data sources — Salesforce, Amadeus, Expedia, and internal platform events — expanding the reach and reliability of the data layer the whole product depends on.
• You'll work across system boundaries: reading Kinesis event schemas and MongoDB data models in the Java platform, then applying that understanding in the Python pipeline codebase — driving schema evolution strategies, data contract enforcement, and backward compatibility across both worlds.
• You'll drive data quality and governance — extending Great Expectations and Data Contract CLI across pipeline tiers, owning Athena views and SQL assets, and building monitoring and alerting for pipeline health, data freshness, and quality drift.
• You'll partner with DevPlatform on event schema design and with data science to operationalise ML workloads, ensuring clean data handoffs across every system boundary.
• You'll work AI-first every day — using Claude Code and MCP tools as a core part of your workflow, contributing to AI-assisted pipeline scaffolding and data discovery tooling alongside a custom multi-agent system built around 17 specialised agents.

What We're Looking For

You may be a good fit if you have:

• 7+ years building production data or backend systems
• Strong proficiency in both Python (PySpark, data engineering) and Java — you can read and navigate Spring/JVM codebases comfortably, not just Python
• Experience with lakehouse or data warehouse architectures on cloud — Iceberg, Delta Lake, Redshift, BigQuery, or Snowflake
• Production experience with AWS data services: Glue, Athena, S3, Lambda
• Experience with workflow orchestration — Airflow, Kestra, Step Functions, or similar
• The ability to work across system boundaries: you understand upstream event schemas, data models, and downstream consumer needs

Strong candidates may also have:

• Hands-on experience with Apache Iceberg — MERGE operations, schema evolution, partition evolution
• Experience with Trino or Presto for federated or interactive SQL analytics at scale
• Experience with dbt for data transformation, modelling, and testing
• Familiarity with data quality frameworks — Great Expectations, Monte Carlo, or similar
• A background in event-driven architectures: Kinesis, Kafka, SQS
• Genuine interest in AI-assisted development and LLM-based engineering workflows
• Familiarity with the hospitality domain or multi-tenant B2B SaaS data challenges

Why Duetto?

• AI-first is the reality, not the roadmap. Every engineer uses Claude Code daily, and you'll be contributing to a custom multi-agent system with 17 specialised agents, human-in-the-loop approval gates, and AI-assisted pipeline generation. This is what modern data engineering looks like.
• Cross-cutting technical ownership. This isn't a siloed data role — you'll operate across Java upstream systems and Python pipelines, with real architectural influence over how data flows through the entire platform.
• The scale and the stakes are real. Millions of pricing decisions processed daily, 80+ integration partners, and a live evolution toward near-real-time streaming. The technical challenges are genuine.
• A team with the right values. Low ego, high EQ, intellectual curiosity, and active mentorship in both directions across a collaborative US and Europe group.
• Modern stack at real scale. Python/PySpark, Apache Iceberg, Airflow, AWS Glue, Terraform, Datadog — tools that matter, used on problems that matter.

The Details

• Location: Croatia (Remote)
• Team: Data Platform
• Direct reports: None
• Travel: None

Duetto is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by applicable law.

Sound like you?

You don't need to be an expert in every technology on this list. If you're a strong engineer who spans Java and Python, cares about data quality, and wants to work somewhere AI is genuinely part of how you build — we'd love to hear from you.

#LI-REMOTE

Are you excited about building analytics foundations that people can truly rely on, turning complex business questions into trusted datasets, metrics, and reporting experiences? At Super Technologies, data is a strategic enabler of decision-making, experimentation, and performance measurement. Our Analytics Platform team builds the foundations that make data reliable, accessible, and actionable, partnering with product, commercial, and engineering teams to measure what matters.

You’ll be joining a mature and growing community of 40+ data engineers and analytics engineers who are shaping one of the most advanced data ecosystems in the industry.

What you'll be doing:

• Design and build curated analytical datasets, metric implementation, and semantic models that enable consistent self-service analytics across the company
• Work hands-on with data warehouse modelling to turn evolving product and business needs into scalable, reusable data structures
• Partner closely with product, data scientists and business stakeholders to clarify requirements and translate them into reliable data models and reporting-ready outputs
• Build and own production dashboards (Tableau) on top of curated datasets and metric definitions, ensuring correctness, performance, and a consistent “single source of truth”
• Contribute to data quality, observability, lineage, and documentation practices to increase trust and reduce firefighting
• Promote reuse over reinvention, identify ad-hoc logic in reporting views or custom SQL and refactor it into curated, high-quality models that multiple teams can rely on.
• Raise the engineering bar through reviews, testing, automation, and pragmatic architectural improvements
• Apply engineering rigor to analytics - treat semantics as code utilizing version control, testing, automation, and CI/CD practices to manage the lifecycle of our business logic.
• Drive self-service adoption - create the foundations that allow analysts and business users to explore data safely and independently, without needing to reinvent the wheel.
• Work embedded within Data Engineering squads, collaborating daily with both data engineers and analysts on shared foundations and end-to-end delivery

We're looking for:

• Strong SQL skills and proven experience building analytical datasets and metrics in a modern data warehouse (Snowflake preferred, BigQuery, Redshift and similar also valuable)
• Solid understanding of data warehouse modeling and best practices (dimensional modeling, fact/dimension design, grains, slowly changing dimensions, semantic consistency)
• Experience working with reporting and BI tools (Tableau preferred), including understanding how data sources, extracts, and dashboard logic impact correctness, performance, and trust
• A production mindset: you care about reliability, maintainability, documentation, and operational ownership, not just creating a dataset once
• Strong ownership and collaboration skills, able to drive clarity in ambiguous problem spaces and partner effectively with both technical and non-technical teams
• Excellent communication skills with a pragmatic, problem-solving approach

Bonus points for:

• Experience with orchestration and workflow tooling (Airflow or similar)
• Experience with data cataloging, lineage, and governance tooling (e.g., DataHub)
• Background in product-led, experimentation-driven, or high-growth environments where definitions evolve quickly
• Experience supporting downstream operational consumers (e.g., CRM/audience platforms) or ML/feature engineering use cases
• Familiarity with streaming/event-heavy ecosystems (Kafka or similar)

Are you excited about building analytics foundations that people can truly rely on, turning complex business questions into trusted datasets, metrics, and reporting experiences? At Super Technologies, data is a strategic enabler of decision-making, experimentation, and performance measurement. Our Analytics Platform team builds the foundations that make data reliable, accessible, and actionable, partnering with product, commercial, and engineering teams to measure what matters.

You’ll be joining a mature and growing community of 40+ data engineers and analytics engineers who are shaping one of the most advanced data ecosystems in the industry.

What you'll be doing:

• Design and build curated analytical datasets, metric implementation, and semantic models that enable consistent self-service analytics across the company
• Work hands-on with data warehouse modelling to turn evolving product and business needs into scalable, reusable data structures
• Partner closely with product, data scientists and business stakeholders to clarify requirements and translate them into reliable data models and reporting-ready outputs
• Build and own production dashboards (Tableau) on top of curated datasets and metric definitions, ensuring correctness, performance, and a consistent “single source of truth”
• Contribute to data quality, observability, lineage, and documentation practices to increase trust and reduce firefighting
• Promote reuse over reinvention, identify ad-hoc logic in reporting views or custom SQL and refactor it into curated, high-quality models that multiple teams can rely on.
• Raise the engineering bar through reviews, testing, automation, and pragmatic architectural improvements
• Apply engineering rigor to analytics - treat semantics as code utilizing version control, testing, automation, and CI/CD practices to manage the lifecycle of our business logic.
• Drive self-service adoption - create the foundations that allow analysts and business users to explore data safely and independently, without needing to reinvent the wheel.
• Work embedded within Data Engineering squads, collaborating daily with both data engineers and analysts on shared foundations and end-to-end delivery

We're looking for:

• Strong SQL skills and proven experience building analytical datasets and metrics in a modern data warehouse (Snowflake preferred, BigQuery, Redshift and similar also valuable)
• Solid understanding of data warehouse modeling and best practices (dimensional modeling, fact/dimension design, grains, slowly changing dimensions, semantic consistency)
• Experience working with reporting and BI tools (Tableau preferred), including understanding how data sources, extracts, and dashboard logic impact correctness, performance, and trust
• A production mindset: you care about reliability, maintainability, documentation, and operational ownership, not just creating a dataset once
• Strong ownership and collaboration skills, able to drive clarity in ambiguous problem spaces and partner effectively with both technical and non-technical teams
• Excellent communication skills with a pragmatic, problem-solving approach

Bonus points for:

• Experience with orchestration and workflow tooling (Airflow or similar)
• Experience with data cataloging, lineage, and governance tooling (e.g., DataHub)
• Background in product-led, experimentation-driven, or high-growth environments where definitions evolve quickly
• Experience supporting downstream operational consumers (e.g., CRM/audience platforms) or ML/feature engineering use cases
• Familiarity with streaming/event-heavy ecosystems (Kafka or similar)