Data Factory Engineer

MSC Cruises; Napoli; Hybrid; EUR 38,000 - 45,000; Full time

Benefits offered by this job

Health Insurance

Learning opportunities

Flexible working hours

Employee Assistance Programme

Discounts & Partnerships within theMSC

Job summary

MSC Cruises is seeking a Data Engineer to design and operate backend data pipelines across on-premises and cloud environments, evolving toward Microsoft Fabric and Databricks. You will implement modern Lakehouse patterns, ensure data quality, and collaborate with Data Architects and engineers.

The role requires 5–7 years in data engineering, strong SQL and Python/PySpark skills, and experience with on-prem and cloud integrations.

Qualifications

• Bachelor's or Master’s in Computer Science, Engineering, Information Systems, or related discipline.
• 5–7 years of professional Data Engineering/ETL experience in complex environments.
• Strong SQL skills, data analysis, and data modeling.
• Hands-on Python and PySpark; Java/Scala a plus.
• Experience with on-prem and cloud data integration (Oracle/SQL Server, SSIS, schedulers).
• Knowledge of Microsoft Fabric and Databricks, Spark, Delta Lake, and Notebooks.

Responsibilities

• Design, build, test, deploy, and maintain backend data pipelines for ingestion, transformation, orchestration, and delivery across on-premises and cloud environments.
• Develop and enhance ETL/ELT solutions for the current on-premises Data Platform.
• Contribute to the Data Platform based on Microsoft Fabric and Databricks.
• Apply data engineering patterns including metadata-driven pipelines and idempotency.
• Translate data designs into robust backend implementations with cross-functional teams.
• Implement automated data quality checks, logging, and operational monitoring.
• Optimize pipelines for performance, scalability, and cost.
• Manage source code with Git-based workflows and CI/CD pipelines.

Skills

SQL skills

Python

PySpark

Git & CI/CD

DataOps

Agile teamwork

English fluency

Education

Bachelor's or Master’s in CS/Engineering

Tools

Spark

Delta Lake

Databricks

OneLake

Fabric Data Factory

Azure Data Factory

SQL Server / Oracle

Job description

Job Purpose

The Data Engineer within the Data Factory pillar is responsible for designing, developing, testing, deploying, and operating backend data integration solutions and reusable data platform components. The role supports the current on-premises data estate while enabling the progressive evolution toward Microsoft Fabric and Databricks. Its purpose is to deliver secure, scalable, observable, and high-quality batch and near-real-time data pipelines that make trusted data available for reporting, analytics, data products, and AI use cases.

Key Accountabilities

• Design, build, test, deploy, and maintain backend data pipelines for ingestion, transformation, orchestration, and delivery across on-premises and cloud environments.


• Develop and enhance ETL/ELT solutions for the current on-premises Data Platform, working with relational databases, enterprise data warehouses, flat files, APIs, scheduling tools, and existing integration technologies.


• Contribute to the target Data Platform based on Microsoft Fabric and Databricks, using modern Lakehouse patterns and services such as OneLake, Fabric Data Factory, notebooks, Spark, Delta Lake, Databricks Workflows, and SQL endpoints, as applicable to the adopted architecture.


• Apply modern data engineering patterns including metadata-driven and configuration-driven pipelines, reusable frameworks, parameterization, incremental processing, change data capture, idempotency, schema evolution, partitioning, and medallion-style data layers.


• Translate logical and physical data designs into robust backend implementations, in collaboration with Data Architects, Data Factory Leads, platform engineers, analysts, and business-facing data teams.


• Implement automated controls for data quality, reconciliation, lineage, logging, alerting, error handling, retry, restartability, and operational monitoring.


• Optimize data pipelines and storage for performance, scalability, resiliency, and cost efficiency, using appropriate workload patterns for batch, micro-batch, or streaming use cases.


• Manage source code through standard Git-based engineering procedures. The use of branching, pull requests, peer review, version control, automated testing, artifact management, and standard CI/CD pipelines for build, release, and deployment is mandatory.


• Create automated unit, integration, regression, and data validation tests to reduce production defects and support repeatable releases across development, test, and production environments.


• Support secure engineering practices, including secrets management, least-privilege access, environment segregation, secure connectivity, and compliance with enterprise data governance and security standards.


• Produce and maintain technical documentation for pipelines, mappings, transformation rules, interfaces, dependencies, deployment procedures, runbooks, and operational support.


• Investigate incidents and data issues through structured root-cause analysis, implement permanent corrective actions, and contribute to continuous improvement of engineering standards and platform reliability.


• Participate in Agile delivery ceremonies, estimation, backlog refinement, technical reviews, release planning, and knowledge-sharing activities within the Data Factory pillar.


• Leverage approved AI-assisted engineering capabilities to accelerate the software development lifecycle, including code generation and refactoring, technical documentation, test-case creation, defect analysis, code review, and release preparation. Ensure that all AI-generated outputs are subject to appropriate human validation, security controls, quality standards, and standard CI/CD governance before deployment.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent professional experience.


• 5–7 years of professional experience in Data Engineering, backend data integration, ETL/ELT development, or enterprise Data Platform delivery within complex environments.


• Strong SQL skills, including data analysis, query optimization, data manipulation, dimensional or relational modelling, and troubleshooting of large data workloads.


• Hands‑on programming and scripting experience, preferably with Python and PySpark; knowledge of Java or Scala is an advantage.


• Experience with on‑premises data integration and database technologies. Knowledge of Oracle, Microsoft SQL Server, SSIS, enterprise schedulers, file‑based interfaces, APIs, or comparable technologies is relevant to the current landscape.


• Knowledge of modern cloud and Lakehouse engineering patterns, with practical exposure to Microsoft Fabric and/or Databricks. Experience with Spark, Delta Lake, OneLake, Fabric Data Factory, Azure Data Factory, notebooks, or workflow orchestration is strongly preferred.


• Mandatory experience managing code through Git‑based repositories and standard CI/CD procedures, including branching, pull requests, automated testing, controlled promotion across environments, and repeatable deployment pipelines.


• Understanding of DataOps and software engineering practices such as modular design, reusable components, coding standards, peer review, test automation, infrastructure/configuration as code, observability, and release management.


• Experience implementing data quality checks, reconciliation controls, logging, monitoring, alerting, and operational support for production data pipelines.


• Understanding of data security, privacy, access control, secrets management, and governance requirements in enterprise data environments.


• Ability to analyse complex issues, perform root‑cause analysis, and balance delivery speed with reliability, maintainability, and technical standards.


• Ability to work effectively in Agile, cross‑functional, and multi‑vendor teams, managing priorities and communicating clearly with technical and non‑technical stakeholders.


• Fluent English, written and spoken; Italian or additional languages are an advantage.


• Nice to have: knowledge of MSC internal tools, Data Platform components, development standards, operational processes, business terminology, and existing data flows.

Nice to have

relevant certifications in Microsoft Fabric, Microsoft Azure, Databricks, data engineering, or DevOps engineering.

VISA Requirements

Right to work in Italy

What we offer

In compliance with the applicable pay transparency legislation, the following information is provided regarding the initial compensation foreseen for the position.

Contract

• Type of contract: Permanent


• Hybrid working model: 2 days per week remotely


• Applicable Collective Labour Agreement (CCNL): “Addetti agli uffici ed ai terminals delle società di navigazione che esercitano l’armamento privato” – sez. personale di terra


• Gross annual salary (RAL): between €38.000 – €45.000 gross per year, paid over 14 monthly installments


• Meal vouchers: worth €8 per on-site working day

The offer in terms of salary will be determined based on the candidate’s professional experience, technical competencies, and the level of autonomy and responsibility assigned to the role.

Welfare&Wellbeing

We offer a range of Welfare&Wellbeing Initiatives as well as benefits including Productivity award, Health Insurance, Learning opportunities, flexible working hours, Employee Assistance Programme, Discounts & Partnerships within the MSC group.

Our commitment

We are committed to building a future that values diverse perspectives, embraces the world beyond borders, and fosters an inclusive environment where every individual feels valued, respected and empowered to be their authentic selves. Our commitment extends to taking meaningful, measurable actions that have a long-term positive impact on our guests, our employees and our planet.