Senior Data Engineer Porto

Senior Data Engineer
Porto
Porto, Porto District, Portugal

At Damia Group Portugal (Permanent), in Porto, Portugal

Expires at: 2026-05-11

Remote policy: Partial remote
Damia Group is an international tech recruitment agency with 3 decades of experience. Our arrival in Portugal, 7 years later, was set on a mission to transform IT recruitment experiences and, through them, achieve better results. We believe in
- term relationships with a transparent and relaxed mindset. In a short period we have reached the hearts of both
- ups and larger organizations by delivering
- on curated candidate shortlists, increased job offer acceptance rates and shorter
-
- fill.

Damia handles direct hires on behalf of our clients.

The client is a global leader in quality of life services, founded in France. The company operates in over 50 countries, offering a wide range of services that include:On-site Services: Catering, cleaning, maintenance, and facilities management. Benefits & Rewards Services: Meal cards, gift cards, mobility solutions, and employee benefits. Personal & Home Services: Concierge, home care, and childcare (less prominent). They focus on improving the quality of life for employees, students, patients, and others across various sectors, including corporate offices, schools, healthcare, defence, and remote sites.

They have been present in Portugal. Core Services, Employee Benefits & Rewards: Well known for offering meal cards like Sodexo Refeição Pass, as well as gift cards and fuel cards.

On-site Services: Catering and facilities management in corporate, healthcare, and educational environments.

Key Clients: Includes multinational corporations, public institutions, and local businesses.

Mission in Portugal: Helping improve employee
- being and organisational performance through tailored workplace and benefits solutions.   About the role/What you'll do:

Privileged technical point of contact for the Product Owner and business sponsors;

Ability to take data engineering responsibility of a data project;

Listening skills and excellent communication skills;

Ability to manage technical debt (variable according to use cases);

Technical architect, lead design meetings and tech breakdowns;

Ability to mentor and guide the work of a more junior Data Engineer

Main requirements

Requirements:

Engineering Master’s degree or Ph
D;

5 years+ experience in a Data engineering role into large corporate organizations;

Experience in a Data/AI environment into a Cloud ecosystem.

Software Engineering & SQL:

Python: Strong coding skills in Python, especially with libraries related to data manipulation (e. g. , Pandas) and interfacing with databases.

Python ecosystem: Strong knowledge of tooling in Python ecosystem such as dependency management tooling (poetry, venv).

Software engineering practices: Strong experience in putting in place good software engineering practices such as design patterns, testing (unit, integration, e2e), clean code, code formatting etc.

SQL: Advanced knowledge of SQL for data querying, transformation, and aggregation.

Data Architecture:

Design: Ability to design scalable and robust data pipelines considering functional and
- functional requirements.

Integration: Knowledge of data architectures to ensure reliable and efficient data flow.

Cloud Platforms:

Azure Services: Proficiency in Azure Data Lake, Azure Data Factory, Azure Blob Storage, Azure SQL

Database, and other related Azure services.

Azure Databricks: Proficiency in using Databricks for deploying & running Spark jobs including advanced usage (such as handling clusters, secret scopes, warehouses, unity catalog etc. )

Cloud Infrastructure: Familiarity with setting up, configuring, and managing virtual networks, VMs, security groups, and related components on Azure is preferable.

Data Processing with Big Data Frameworks:

Spark: Mastery of Py
Spark for data processing, particularly Data
Frame API.

Delta Lake: Understanding of Delta Lake for reliable data lakes.

Data Storage & Data Management.

Database Management: Knowledge of both relational (SQL Server) and No
SQL databases (like Cosmos
DB).

Data Formats: Familiarity with different data storage formats such as Parquet, JSON, CSV, and Delta.

Data Integration Tools:

Azure Data Factory: Skill in designing, deploying, and managing data integration solutions with ADF.

Data Modelling: Schema Design: Ability to design efficient and scalable database schemas for both operational and analytical use cases.

Should know Dimensional Modelling, 3NF (Third Normal Form) Data Modelling and Data Vault Modelling.

ETL Design: Knowledge in designing Extract, Transform, Load (ETL) processes.

DBT: Used to model, transform and test data on specific data products.

Performance Optimisation:

Query Tuning: Skills in optimising complex SQL queries.

Pipeline Optimisation: Knowledge in optimising data processing pipelines, particularly in Databricks/Spark.

Security & Compliance:

Data Security: Knowledge of encryption techniques, both
- rest and
- transit.

Access Control: Understanding of
- based access controls and integration with Azure Active Directory.

Dev
Ops & Automation :

CI/CD: Experience with continuous integration and continuous deployment tools like Azure Dev
Ops.

Infrastructure as Code: Familiarity with tools like Azure Resource Manager (ARM) templates or Terraform is a plus

Containerization: Basic understanding of Docker and Kubernetes, especially as they might integrate with Azure services is a plus.

Monitoring & Troubleshooting:

Logging & Monitoring: Familiarity with tools like Azure Monitor, Log Analytics, or other monitoring solutions.

Debugging: Ability to troubleshoot and debug issues within data pipelines and storage systems.