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Codurance

Senior Data Engineer – AI

Codurance

. Work as part of software delivery teams .

Posted 9/29/2026full-timeRemote • PortugalSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building production-grade data systems using Databricks, Python, and cloud platforms like AWS or Azure. Proficient in data engineering practices, including data quality, orchestration, and ML/AI feature pipelines.

Highest-signal resume keywords
Databricks EngineeringPython ProgrammingData Quality ManagementAWS or Azure ExperienceML/AI Feature Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data EngineeringDelta LakeSpark/PySparkGitCI/CDInfrastructure as CodeData OrchestrationMonitoringSchema EvolutionMLflow
Soft Skills
Client DeliveryStakeholder EngagementMentoringCollaboration
Tools & Technologies
DatabricksUnity CatalogProduction LakehouseCode ReviewDeployment
Industry Keywords
Data FoundationsModernisation ProjectsAI-ReadinessData ContractsModel Lineage

Tech Stack

Tools & technologies
AWSAzureCloudPySparkPythonSparkUnity

About the role

Key responsibilities & impact
  • Work as part of software delivery teams
  • Help clients build production-grade data foundations
  • Contribute to modernisation, platform, data, and AI-readiness engagements
  • Use Databricks as the primary data engineering platform
  • Build durable, well-crafted data systems with production focus

Requirements

What you’ll need
  • Current or recent experience as a Data Engineer, Senior Data Engineer, Data Platform Engineer, or Databricks Engineer
  • Hands-on experience with Databricks in production, including Delta Lake, Spark/PySpark, Unity Catalog, or production lakehouse projects
  • Strong experience with Python, testing, Git, CI/CD, code review, deployment, or Infrastructure as Code
  • Experience with data quality, orchestration, monitoring, lineage, schema evolution, or data contracts
  • Experience with client delivery, stakeholder engagement, modernisation projects, mentoring, or working across engineering teams
  • Experience with AWS or Azure; either cloud is acceptable
  • Experience with ML/AI feature pipelines, MLflow, model lineage, embeddings, vector search, retrieval, or evaluation datasets