Senior Data Engineer

FCamara EuropeData engineering

This role is currently limited to individuals currently and lawfully residing with a work permit in the following countries: Portugal, Bulgaria, Croatia, Estonia, Hungary, Latvia, Lithuania, Slovakia, and Romania.ContractSenior

Salary not disclosed

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Job Details

Languages: Fluent English; minimum B2 level (professional working proficiency).
Experience: At least 6+ years of experience in similar roles, including 3+ years working with Databricks in production environments.
Required Skills: PythonAzureTerraformDatabricksPySpark

Requirements

Have at least 6+ years of experience in similar roles, including 3+ years working with Databricks in production environments.
Bring strong hands-on production experience with Databricks, PySpark, Spark SQL, Delta Lake, and Delta Live Tables/Lakeflow.
Have experience designing and optimising batch and streaming pipelines and evolving Lakehouse architectures across bronze, silver, and gold layers.
Have implemented CI/CD for data workloads using Git, Azure DevOps or GitHub Actions, and Databricks Asset Bundles.
Have practical DataOps experience, including automated testing, pipeline versioning, observability, and monitoring.
Have hands-on data governance experience, ideally with Unity Catalog access control, lineage, and auditing.
Have experience with Infrastructure as Code using Terraform.
Have strong proficiency in Python, PySpark, and advanced SQL.
Have cloud experience, preferably with Azure services including ADLS, Data Factory, Key Vault, and Entra ID.
Have experience with agentic coding.
Be fluent in English to work with global teams and clients; the role requires a minimum B2 level.
Databricks Data Engineer Professional or Azure certifications (DP-203 / DP-700) are appreciated.
Experience with multi-tenant SaaS platforms and per-client data isolation is appreciated.
Knowledge of MLOps, MLflow, Feature Store, Model Serving, generative AI/RAG data pipelines, Microsoft Purview, data quality tools, or streaming technologies is appreciated.

Responsibilities

Design, build, and optimise batch and streaming data pipelines on Databricks using PySpark, Spark SQL, Delta Lake, and Delta Live Tables.
Evolve Lakehouse 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 for LGPD and GDPR compliance.
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.
Review code, mentor mid-level and junior engineers, and collaborate with product, data science, and consulting teams.
Document architecture decisions, technical standards, and data engineering best practices.

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