Senior Data Engineer (remote from Portugal or selected EU countries)

We are looking for Senior Data Engineer with strong English skills to join us in a project with a Europe-based company. This is a remote position, and we will consider candidates from Portugal and a limited set of EU countries.

Our Client is a global player in the field of operations, risk, and sustainability services and solutions for companies and industries across the world. As they provide both services and technology for their clients, the Senior Data Engineer we are looking for will support the delivery of value across client's operations and technology ecosystem.

In this position, you are hired by FCamara Europe, the European division of one of the largest and fast-growing tech and cognitive services group in Brazil, with +1500 specialists and engineers. From Portugal we deliver services to the EU, UK, US and MENA, bringing together multicultural, diverse and creative teams through projects, managed services, squads, and staff augmentation.

Responsibilities

In this role, you will be primarily responsible for acting as the technical reference for our Client’s Databricks data platform, designing and operating reliable data pipelines, implementing DataOps and CI/CD practices, and establishing strong data governance to ensure secure, traceable, and AI-ready datasets.

In a typical week you will:

• Design, build, and optimise batch and streaming data pipelines on Databricks using PySpark, Spark SQL, Delta Lake, and Delta Live Tables.
• Evolve the Lakehouse architecture (bronze, silver, and gold layers) to ingest and transform operational, behavioural, and HSE data from multiple clients and sources.
• Implement and maintain CI/CD pipelines for data workloads using Databricks Asset Bundles, Git, Azure DevOps, or GitHub Actions.
• Automate data testing, quality checks, and promotion across development, staging, and production environments.
• Implement and manage Unity Catalog, including catalogs, permissions, lineage, auditing, and data access policies.
• Define and enforce data governance standards, security controls, data masking, and per-client data isolation to ensure compliance with LGPD and GDPR.
• Monitor pipeline performance, define SLAs, set up alerts, and lead incident reviews.
• Provision infrastructure as code using Terraform and standardise pipeline templates and best practices.
• Act as a technical reference by reviewing code, mentoring mid-level and junior engineers, and collaborating with product, data science, and consulting teams.
• Document architecture decisions, technical standards, and data engineering best practices.

Requirements

Seniority: Due to the nature of the position, we are looking for someone with at least 6+ years of experience in similar roles, including 3+ years working with Databricks in production environments.

To be considered for the role, it is mandatory you to fulfill these requirements:

• Strong hands-on experience with Databricks in production, including PySpark, Spark SQL, Delta Lake, and Delta Live Tables / Lakeflow.
• Solid experience designing and optimising batch and streaming pipelines and evolving Lakehouse architectures (bronze, silver, gold layers).
• Proven experience implementing CI/CD for data workloads using Git, Azure DevOps or GitHub Actions, and Databricks Asset Bundles.
• Practical DataOps experience, including automated testing, pipeline versioning, observability, and monitoring.
• Hands-on experience with data governance, ideally using Unity Catalog (access control, lineage, and auditing).
• Experience with Infrastructure as Code using Terraform.
• Strong proficiency in Python, PySpark, and advanced SQL.
• Cloud experience, preferably on Azure (ADLS, Data Factory, Key Vault, Entra ID).
• Experience with agentic coding.
• Fluent English to work with global teams and clients.

It will be very well appreciated if you also bring:

• Databricks Data Engineer Professional certification or Azure certifications (DP-203 / DP-700).
• Experience with multi-tenant SaaS platforms and per-client data isolation.
• Knowledge of MLOps practices (MLflow, Feature Store, Model Serving) and building data pipelines for generative AI / RAG.
• Experience with Microsoft Purview.
• Experience with data quality tools (Great Expectations, Lakehouse Monitoring, dbt).
• Experience with streaming technologies (Kafka, Event Hubs, or Structured Streaming).
• Domain knowledge in HSE, industrial operations, or risk management data.

Beyond technical knowledge, we also expect you to have a collaborative and team-oriented profile:

• Ability to understand requirements and technical solutions
• Strong communication skills
• Critical thinking and a focus on continuous improvement
• Strong delivery mindset and commitment to deadlines and goals