Are you excited about building modern data infrastructure that powers self-service analytics and high-quality data products at scale? 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 closely with product, analysts, data scientists, and engineering teams to measure what matters.

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

What you'll be doing:

• Design, build, and evolve scalable ETL/ELT pipelines for high-volume, fast-changing datasets
• Own medium-to-large data initiatives end-to-end, from requirement shaping and technical design to production rollout and ongoing improvement
• Model reliable, analytics-ready data using proven warehouse methodologies such as Kimball, Inmon, or similar approaches
• Develop and maintain curated analytical layers, semantic models, and data marts that enable self-service and trusted decision-making
• Orchestrate robust workflows using tools such as Apache Airflow and improve reliability, observability, and maintainability of data pipelines
• Partner with software engineers, analysts, product managers, and business stakeholders to translate ambiguous requirements into scalable data solutions
• Proactively identify data quality, performance, and modeling issues and drive improvements before they become business problems
• Contribute to architecture and engineering decisions within your domain, balancing speed, quality, and long-term maintainability
• Mentor less senior data engineers through code reviews, design discussions, documentation, and day-to-day guidance
• Raise the engineering bar through testing, automation, CI/CD practices, clear documentation, and thoughtful technical standards

We're looking for:

• Strong SQL and Python skills, with solid hands-on experience building production-grade data pipelines and analytical models
• Deep understanding of dimensional modeling methodologies and modern data warehouse design in platforms such as Snowflake, BigQuery, or similar
• Experience owning projects independently and driving delivery across multiple stakeholders or teams
• Solid experience with orchestration tooling such as Airflow, Dagster, or similar
• Strong understanding of data quality, lineage, observability, and operational excellence in a data engineering environment
• Ability to take ambiguous business problems and turn them into clean, maintainable, well-documented data solutions
• Experience working with cloud platforms, preferably AWS, though Azure or GCP experience is also valuable
• Confidence influencing technical decisions within your team and collaborating effectively with both technical and non-technical stakeholders
• Strong communication skills and a collaborative, problem-solving mindset

Bonus points for:

• Experience with event-driven source systems
• Experience with cloud-native analytical databases like Snowflake, BigQuery, Databricks
• Familiarity with semantic layers, metrics modeling, and enabling data products beyond traditional BI reporting
• Exposure to experimentation-driven or product-led environments
• Experience with data governance, cataloging, and documentation practices such as DataHub
• Experience improving developer productivity through tooling, shared frameworks, or reusable patterns
• Multi-cloud exposure and understanding of platform trade-offs

Are you excited about building modern data infrastructure that powers self-service analytics and high-quality data products at scale? 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 closely with product, analysts, data scientists, and engineering teams to measure what matters.

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

What you'll be doing:

• Design, build, and evolve scalable ETL/ELT pipelines for high-volume, fast-changing datasets
• Own medium-to-large data initiatives end-to-end, from requirement shaping and technical design to production rollout and ongoing improvement
• Model reliable, analytics-ready data using proven warehouse methodologies such as Kimball, Inmon, or similar approaches
• Develop and maintain curated analytical layers, semantic models, and data marts that enable self-service and trusted decision-making
• Orchestrate robust workflows using tools such as Apache Airflow and improve reliability, observability, and maintainability of data pipelines
• Partner with software engineers, analysts, product managers, and business stakeholders to translate ambiguous requirements into scalable data solutions
• Proactively identify data quality, performance, and modeling issues and drive improvements before they become business problems
• Contribute to architecture and engineering decisions within your domain, balancing speed, quality, and long-term maintainability
• Mentor less senior data engineers through code reviews, design discussions, documentation, and day-to-day guidance
• Raise the engineering bar through testing, automation, CI/CD practices, clear documentation, and thoughtful technical standards

We're looking for:

• Strong SQL and Python skills, with solid hands-on experience building production-grade data pipelines and analytical models
• Deep understanding of dimensional modeling methodologies and modern data warehouse design in platforms such as Snowflake, BigQuery, or similar
• Experience owning projects independently and driving delivery across multiple stakeholders or teams
• Solid experience with orchestration tooling such as Airflow, Dagster, or similar
• Strong understanding of data quality, lineage, observability, and operational excellence in a data engineering environment
• Ability to take ambiguous business problems and turn them into clean, maintainable, well-documented data solutions
• Experience working with cloud platforms, preferably AWS, though Azure or GCP experience is also valuable
• Confidence influencing technical decisions within your team and collaborating effectively with both technical and non-technical stakeholders
• Strong communication skills and a collaborative, problem-solving mindset

Bonus points for:

• Experience with event-driven source systems
• Experience with cloud-native analytical databases like Snowflake, BigQuery, Databricks
• Familiarity with semantic layers, metrics modeling, and enabling data products beyond traditional BI reporting
• Exposure to experimentation-driven or product-led environments
• Experience with data governance, cataloging, and documentation practices such as DataHub
• Experience improving developer productivity through tooling, shared frameworks, or reusable patterns
• Multi-cloud exposure and understanding of platform trade-offs

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)

Are you excited about building modern data infrastructure that empowers self-service analytics at scale? 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 and business teams to measure what matters.
You’ll be joining a mature and growing community of 40+ data engineers who are shaping one of the most advanced data ecosystems in the industry.

What you'll be doing:

• design, build, and optimize scalable ETL/ELT pipelines for high-volume, fast-changing datasets
• apply proven data warehouse methodologies (e.g. Kimball, Inmon) to design and deliver trustworthy, performant data models
• develop and maintain semantic layers and curated data marts to enable analytical self-service across the company
• orchestrate reliable workflows with modern tooling such as Apache Airflow
• enforce data quality, lineage, and observability practices to ensure confidence in our data products
• partner with product, analytics, and engineering teams to co-create scalable data solutions that deliver measurable business value
• raise the engineering bar through code reviews, testing, automation, and architectural improvements

We're looking for:

• strong SQL and Python skills, with solid understanding of distributed data systems and modern data architectures
• deep knowledge of dimensional data modeling (Kimball, Inmon) and hands-on experience building analytical layers in DWHs such as Snowflake or BigQuery
• hands-on experience with cloud platforms (AWS preferred, Azure/GCP also valuable)
• workflow orchestration experience with Airflow or similar tools
• strong ownership mindset—able to work independently, structure work, and partner effectively with technical and non-technical stakeholders
• excellent communication skills with a collaborative, problem-solving approach
• based in Zagreb as this will require hybrid work

Bonus points for:

• experience with real-time streaming technologies (Kafka or similar)
• experience with cloud native Analytical databases like Snowflake
• multi-cloud exposure and understanding of platform trade-offs
• background in product-led or experimentation-driven environments
• experience with data governance, cataloging, and documentation practices (e.g. DataHub)

Are you excited about building modern data infrastructure that empowers self-service analytics at scale? 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 and business teams to measure what matters.
You’ll be joining a mature and growing community of 40+ data engineers who are shaping one of the most advanced data ecosystems in the industry.

What you'll be doing:

• design, build, and optimize scalable ETL/ELT pipelines for high-volume, fast-changing datasets
• apply proven data warehouse methodologies (e.g. Kimball, Inmon) to design and deliver trustworthy, performant data models
• develop and maintain semantic layers and curated data marts to enable analytical self-service across the company
• orchestrate reliable workflows with modern tooling such as Apache Airflow
• enforce data quality, lineage, and observability practices to ensure confidence in our data products
• partner with product, analytics, and engineering teams to co-create scalable data solutions that deliver measurable business value
• raise the engineering bar through code reviews, testing, automation, and architectural improvements

We're looking for:

• strong SQL and Python skills, with solid understanding of distributed data systems and modern data architectures
• deep knowledge of dimensional data modeling (Kimball, Inmon) and hands-on experience building analytical layers in DWHs such as Snowflake or BigQuery
• hands-on experience with cloud platforms (AWS preferred, Azure/GCP also valuable)
• workflow orchestration experience with Airflow or similar tools
• strong ownership mindset—able to work independently, structure work, and partner effectively with technical and non-technical stakeholders
• excellent communication skills with a collaborative, problem-solving approach
• based in Madrid or surrounding area as this will require hybrid work

Bonus points for:

• experience with real-time streaming technologies (Kafka or similar)
• experience with cloud native Analytical databases like Snowflake
• multi-cloud exposure and understanding of platform trade-offs
• background in product-led or experimentation-driven environments
• experience with data governance, cataloging, and documentation practices (e.g. DataHub)