Scale Engineer

Senior Data Engineer

Data Engineer · Senior · Full Time

Milan, ItalyEUR 51k – 67k1mo ago
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Role

What you'll do.

As a Senior Data Engineer at Satispay, you will architect and evolve a next-generation data lakehouse platform while empowering Data Analysts and business teams to autonomously model and analyze financial data. This role combines strategic platform ownership with hands-on technical leadership, requiring expertise in distributed systems, real-time streaming, and modern data stack technologies including Amazon Redshift, Apache Airflow, and dbt frameworks. You will drive innovation in machine learning infrastructure, data governance, and AI-assisted engineering while mentoring junior engineers in a fast-paced fintech environment.

Responsibilities

  • Data Lakehouse Architecture & Redshift Platform Ownership: Lead the design, implementation, and evolution of Satispay's data lakehouse strategy, ensuring seamless integration with Amazon Redshift and Redshift Serverless for high-performance analytics at scale. Architect cost-efficient approaches to handle the data demands of a growing 6-million-user fintech platform while maintaining query performance and data integrity across all analytics workloads.
  • Analytics Engineering & dbt Framework Leadership: Establish and maintain the core dbt framework that empowers Data Analysts to independently contribute to data modeling. Implement CI/CD pipelines, comprehensive testing frameworks, and documentation standards that make analytics engineering decentralized, scalable, and maintainable. Champion best practices that enable self-service analytics across the organization.
  • Orchestration & Workflow Automation with Apache Airflow: Build, scale, and maintain reliable orchestration layers using Apache Airflow, designing and optimizing complex directed acyclic graphs (DAGs) that efficiently handle both batch and near-real-time workloads. Ensure robust monitoring, alerting, and recovery mechanisms that meet critical service level agreements for data delivery.
  • Machine Learning Platform & Feature Store Management: Own, architect, and scale Satispay's machine learning platform infrastructure, including the Feature Store that serves model training and real-time inference pipelines. Build robust data pipelines that provision collaborative analytics environments for Data Scientists and Analysts, enabling rapid experimentation and model deployment in production.
  • Real-Time Streaming & Transformation Pipelines: Design and operate streaming transformation pipelines using Apache Flink or Spark Streaming that power near-real-time analytics and feed the feature store. Move the platform beyond batch processing to support real-time financial data insights, event-driven architectures, and immediate decision-making capabilities.
  • AI-Assisted Development & Agentic Engineering Integration: Drive team-wide productivity improvements by evaluating, integrating, and maintaining AI-assisted development workflows. Leverage tools like Claude Code and deploy AI agents to automate routine data engineering tasks, enhance pipeline monitoring, and accelerate delivery cycles while maintaining code quality and governance standards.
  • Data Ingestion & Integration Management: Supervise managed ingestion workflows using Fivetran and maintain custom Python pipelines that ensure cost-efficient, resilient, and reliable data flow across the platform. Design robust error handling and retry mechanisms that guarantee data completeness and timeliness for downstream consumers.
  • Performance Optimization & Cost Management: Design and optimize the entire data platform for performance and cost efficiency across ingestion streams, transformations, feature store operations, and Redshift Serverless. Monitor and continuously improve query performance, storage utilization, and compute costs while maintaining service level objectives.
  • Data Governance, Security & Financial Compliance: Design and implement robust data governance frameworks, access control mechanisms, and privacy measures across all data assets. Ensure strict compliance with financial regulations (PSD2, GDPR, and other relevant standards) while maintaining data security and building trust with regulatory stakeholders.
  • Technical Mentorship & Engineering Standards Leadership: Act as a technical mentor for junior and mid-level data engineers, fostering their growth through code reviews, architectural discussions, and knowledge sharing. Champion documentation practices, establish engineering standards, and create processes that elevate the capabilities of the entire data engineering team.

Qualifications

What we look for.

Technical

  • Advanced SQL & Data Manipulation

    Expert-level proficiency in SQL for complex data manipulation, analysis, and query optimization. Demonstrated ability to write performant SQL across analytical databases, including window functions, optimization techniques, and query execution plan analysis.

  • Python for Data Engineering

    Strong hands-on experience building production-grade data pipelines, data processing applications, and infrastructure automation using Python. Proficiency with libraries like Pandas, PySpark, and custom API integration frameworks.

  • Apache Airflow Orchestration

    Proven expertise designing, building, and maintaining complex Apache Airflow DAGs in production environments. Experience with dynamic DAG generation, monitoring, alerting, and scaling Airflow clusters to handle high-volume workloads.

  • dbt Analytics Engineering Framework

    Deep knowledge of dbt project structure, macros, testing frameworks, and CI/CD integration. Ability to establish dbt best practices, code organization patterns, and documentation standards that enable team scalability.

  • Amazon Redshift & Data Warehousing

    Hands-on experience with Amazon Redshift architecture, query optimization, and Redshift Serverless deployments. Understanding of distribution keys, sort keys, compression, vacuum operations, and cost optimization strategies specific to Redshift environments.

  • Real-Time Streaming Technologies

    Production experience with Apache Flink, Spark Streaming, or equivalent streaming frameworks. Understanding of event processing, stateful computations, windowing, and building low-latency data pipelines for real-time analytics.

  • Feature Store Architecture

    Experience designing, implementing, or operating feature stores that serve machine learning models. Understanding of feature management, versioning, offline-to-online consistency, and integration with ML training and inference pipelines.

  • Data Quality & Observability

    Expertise implementing data quality frameworks, data lineage tracking, and comprehensive monitoring solutions. Experience with tools like dbt testing, Great Expectations, and custom observability platforms to ensure data reliability.

  • Cloud Data Platform Architecture

    Comprehensive understanding of modern cloud data stack architectures, including ETL/ELT patterns, lakehouse designs, data mesh principles, and self-service analytics platforms. Experience migrating or building platforms in AWS environments.

  • Data Security & Compliance Engineering

    Knowledge of implementing data governance, access control mechanisms, encryption strategies, and compliance frameworks. Experience with PSD2, GDPR, and other financial regulations in regulated environments.

Education

  • Computer Science or Engineering Degree

    Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or related field. Advanced degree (Master's) is valued but not required if compensated by exceptional industry experience.

  • Continuous Learning & Certifications

    Demonstrated commitment to continuous learning through relevant certifications, courses, or self-directed study. AWS certifications (Solutions Architect, Data Engineer), dbt certification, or Apache Airflow expertise are valued indicators of commitment to technical excellence.

Experience

  • Enterprise Data Platform Design & Scaling

    Minimum 5+ years of hands-on experience designing, building, and scaling complex enterprise data platform architectures. Demonstrated success leading platform evolution from centralized to distributed architectures serving dozens to hundreds of analytics users.

  • Production Data Engineering at Scale

    Proven track record building and maintaining mission-critical data pipelines and platforms in production environments. Experience managing platforms that process billions of records, serve hundreds of daily queries, and maintain strict SLAs.

  • Leadership & Mentorship

    Experience mentoring junior and mid-level engineers, establishing engineering standards, and driving technical decisions that impact team capability and platform quality. Demonstrated ability to balance hands-on contribution with team leadership.

  • Cross-Functional Collaboration

    Proven ability working closely with Data Analysts, Data Scientists, business stakeholders, and backend engineers. Experience translating business requirements into technical architecture and communicating complex technical concepts to non-technical audiences.

  • FinTech or Regulated Industry Experience

    Preferred experience in fintech, payments, or other highly regulated industries. Understanding of financial compliance requirements, transaction processing, and the operational demands of financial services platforms.

Skills

Required

  • SQL

    Expert-level SQL proficiency for complex analytical queries, optimization, and data modeling

  • Python

    Production-grade Python programming for building scalable data pipelines and automation

  • Apache Airflow

    Demonstrated expertise building, scaling, and maintaining Airflow DAGs in production environments

  • dbt

    Strong hands-on experience with dbt project architecture, testing, and CI/CD integration

  • Amazon Redshift

    Production experience with Redshift architecture, optimization, and Redshift Serverless deployments

  • Data Architecture & Design

    Comprehensive understanding of batch and real-time data architectures, data modeling, and platform design patterns

  • Data Quality & Governance

    Experience implementing data quality frameworks, monitoring, and governance controls

  • Cloud Engineering (AWS)

    Hands-on AWS experience with data services, infrastructure, and cloud architecture patterns

  • Italian & English Fluency

    Fluent in both Italian and English for seamless communication with international teams and stakeholders

Preferred

  • Apache Flink or Spark Streaming

    Nice to have

    Production experience building real-time streaming pipelines and event processing systems

  • Java or Scala

    Nice to have

    Experience with Java or Scala for building robust data pipeline components and backend services

  • Feature Store Implementation

    Nice to have

    Hands-on experience designing or operating feature stores for machine learning platforms

  • Data Mesh Architecture

    Nice to have

    Understanding of data mesh principles, federated data ownership, and decentralized data platform design

  • Fivetran or Managed Ingestion Tools

    Nice to have

    Experience configuring and managing Fivetran connectors or similar managed ingestion solutions

  • Machine Learning Platform Engineering

    Nice to have

    Experience building infrastructure and pipelines that support ML model development, training, and deployment

  • AI-Assisted Development Tools

    Nice to have

    Familiarity with Claude Code, GitHub Copilot, or other AI coding assistants for development acceleration

  • FinTech Domain Knowledge

    Nice to have

    Experience in payments, banking, or financial technology platforms and their operational requirements

  • Kubernetes & Container Orchestration

    Nice to have

    Experience deploying and scaling data workloads using containerization and orchestration platforms

  • Great Expectations or dbt Testing

    Nice to have

    Advanced data quality testing and validation framework expertise

Compensation

Pay and benefits.

Base·EUR 51,000 – 66,500

Full posting

Original listing.

About us

Satispay began by rethinking the simple act of a payment to remove the friction from our daily routines. But we didn’t stop there. Today, we are building a complete financial platform designed to empower people and concretely improve their lives. By giving our 6 million users a clear, open path to pay, save, and invest, we are evolving into the definitive destination for every financial need.

What you'll be doing

As our Senior Data Engineer, you won't just maintain pipelines; you will shape the evolution of our next-generation data platform. You will architect, scale, and govern an ecosystem that empowers Data Analysts and business teams to autonomously model, analyse, and leverage data safely. Here's what your day-to-day will look like:

  • Data Platform Architecture & Evolution - Lead the design and implementation of our modern Data Lakehouse strategy, ensuring seamless integration with Amazon Redshift & Redshift Serverless for high-performance analytics.

  • Self-Service Enablement & Analytics Engineering - Empower Data Analysts to contribute directly to data modelling. You will own the core dbt framework, establishing best practices, CI/CD pipelines, and robust testing frameworks to make analytics engineering scalable and decentralised.

  • Orchestration & Workflow Automation - Build, scale, and maintain reliable orchestration layers using Apache Airflow, optimising complex DAGs for both batch and near-real-time workloads.

  • ML Platform Ownership & Feature Store Management - Own, architect, and scale Satispay’s Machine Learning Platform. You will manage our Feature Store, build robust data pipelines for model training and inference, and provision collaborative analytics environments to empower Data Scientists and Analysts.

  • Streaming & Real-Time Data - Design and operate streaming transformation pipelines (e.g. Apache Flink, Spark Streaming) that power near-real-time analytics and feed the feature store, moving the platform beyond batch.

  • AI Tooling & Agentic Engineering - Drive team-wide productivity gains by exploring, integrating, and maintaining AI-assisted development workflows. This includes leveraging tools like Claude Code and deploying AI agents to automate data engineering tasks, optimise pipeline monitoring, and accelerate delivery.

  • Ingestion & Integration - Supervise managed ingestion workflows (Fivetran) and custom Python pipelines, ensuring cost-efficient, resilient, and reliable data flow across systems.

  • Performance, Cost & Reliability - Design for performance and cost across the entire platform – ingestion streams, transformations, feature store, and Redshift Serverless. Own data quality, observability, and the SLAs your consumers depend on.

  • Data Governance, Security & Compliance - Design robust data security, access control, and privacy measures across all data assets to ensure strict compliance with financial regulations while maintaining trust.

  • Mentorship & Tech Leadership - Act as a technical mentor for junior/mid-level engineers, champion documentation, and establish engineering standards that elevate the entire team’s capabilities.

Who we're looking for

We need a problem-solver who loves teamwork and gets things done. If you're curious and ready for real ownership, you'll fit in! Does this sound like you?

  • Relevant education and technical background - You have a degree in Computer Science, Engineering, or a related field, backed by 5+ years of hands-on experience designing, scaling, and maintaining complex enterprise data platform architectures.

  • Programming Expertise - You are highly skilled in SQL for data manipulation and analysis and have practical experience with at least one of the following: Python, Java, or Scala for building robust data pipelines.

  • Architectural Knowledge - You are familiar with both batch and real-time streaming data architectures and understand the principles behind designing scalable and resilient data systems.

  • Data-Oriented Mindset - You have a strong passion for deeply understanding data, its meaning, the critical importance of preparing it correctly, and the methodologies for ensuring its quality and unwavering consistency.

  • Platform & Mesh Mindset - You understand how to transition from centralised data delivery to a platform model, designing tools and guardrails that allow Data Analysts to safely self-serve and own their data models.

  • Forward-Thinking & AI Curious - You are excited about the intersection of data engineering and AI, with an interest in LLMs, and integrating AI coding assistants (like Claude Code) into the development lifecycle.

  • Structured communication and stakeholder management - You communicate clearly and persuasively, adapting your approach to build trust and engage effectively with diverse stakeholders across technical and business teams.

  • Language proficiency - You are fluent in both Italian and English, enabling seamless communication within our international team and with stakeholders.

  • Passion for FinTech or Startup environments - You are genuinely passionate about the fast-paced and innovative FinTech or startup ecosystem and are eager to contribute to our mission.

CareAbout: how we support your impact

We move fast, and evolution never stops. It’s a fun ride, but it can be challenging. To make sure our people truly thrive, we’re committed to making their lives easier, both in the office and out in the world. That’s why we created CareAbout:

  • ❤️ Health (Private insurance for you and your family, psychological support with Serenis, mental health workshops)

  • 💰 Financial resources (Stock Option Plan, Meal vouchers, Relocation support if you’re moving countries)

  • ⚙️ Growth and development (Professional development programs, Internal mobility, Language courses with Preply)

  • 🌱 Flexibility (Unlimited PTO, Hybrid working policy*, Flexible working hours)

  • 👨‍👩‍👧 Family (Enhanced parental leave, Additional leave for child sickness)

*We embrace three days per week in-office (Tuesday and Thursday + 1 of your choice), with the option to request extra remote time.

Salary: €51,000 - €66,500 gross per annum.

This range is set using objective, gender-neutral criteria for the role's core requirements. However, we do not believe in putting a ceiling on talent. The final compensation package is tailored according to your unique experience and expertise. If your skills go beyond the standard profile, apply anyway so we can discuss a package that reflects your true value.

Equal opportunity employer

At Satispay, we're proud to be an equal opportunity employer. We celebrate diversity and inclusion, welcoming individuals of all backgrounds. This opportunity is open to everyone, regardless - for instance - of race, colour, religion, sex, gender identity, sexual orientation, and national origin. Join us in a workplace where everyone belongs!

Learn more about us

Our values and pillars aren’t just fancy words on a page - they really shape everything we do.

Explore them here.


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