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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Data Engineering with a focus on Databricks, Spark, and Delta Lake, while ensuring the design and implementation of robust Data architectures for Analytics and Machine Learning. Proven ability to lead teams, optimize data processing, and uphold governance and quality standards in high-volume environments.
Highest-signal resume keywords
Databricks ExpertiseSpark ProficiencyData Engineering ExperienceCI/CD ImplementationCloud Environment Proficiency
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data EngineeringData ModelingSparkDelta LakePythonSQLCI/CDDataOpsAnalytics DesignMachine Learning
Soft Skills
Team LeadershipTechnical AuthorityClient EngagementProblem SolvingCollaboration
Tools & Technologies
DatabricksLakehouse ArchitectureAzureAWSGCPUnity CatalogMonitoring ToolsOrchestration Tools
Certifications & Qualifications
Databricks Certification
Industry Keywords
Data GovernanceData QualityPerformance OptimizationIndustrialization PracticesHigh-Volume Environments
Tech Stack
Tools & technologiesAWSAzureCloudFluxGoogle Cloud PlatformPythonSparkSQLUnity
About the role
Key responsibilities & impact- Understand business challenges and translate them into concrete Data solutions
- Design robust, scalable, and secure Data architectures based on Databricks and Lakehouse architectures
- Define technical choices, the technology stack, and development standards
- Design and oversee high-performance, industrialized data pipelines
- Implement best practices for Spark, Delta Lake, Unity Catalog, and the Databricks ecosystem
- Ensure the quality, performance, reliability, and governance of data processing
- Design Data platforms to support Analytics, Machine Learning, and generative AI use cases
- Collaborate with Data Science and AI teams to industrialize the data and flows required for AI models and applications
- Ensure solutions move from design to production through CI/CD, monitoring, and DataOps
- Serve as the technical authority for Data Engineering and Databricks topics
- Lead and support Data Engineers in their technical decisions and professional development
- Promote best practices and contribute to the practice’s technical standards
- Contribute to responses to tenders and commercial proposals
- Challenge client requirements and propose high-value Data solutions
- Support clients with Databricks, Lakehouse architectures, and the industrialization of their Data platforms
Requirements
What you’ll need- University degree, engineering degree, or equivalent master’s-level qualification
- Significant experience in Data Engineering, ideally in complex or high-volume environments
- 5 to 10 years of experience, as indicated in the job header
- Proven expertise in Databricks and hands-on experience using it in production
- Strong skills in Spark, Delta Lake, data modeling, and Lakehouse architectures
- Proficiency in at least one Cloud environment: Azure, AWS, or GCP
- Strong proficiency in Python and SQL
- Proficiency in industrialization practices: CI/CD, orchestration, monitoring, and DataOps
- Understanding of governance, quality, security, and performance optimization challenges
- Ability to design Data platforms for Analytics, Machine Learning, or generative AI use cases
- Ability to challenge technical decisions and support a team
- Ability to serve as a technical authority for teams and clients
- Databricks certification is a strong plus
Benefits
Comp & perks- Rapid professional development through Niji University, with more than 33,000 learning resources, and hands-on experience
- Access to the latest AI tools for teams and experts
- Personalized support: regular one-to-one meetings, a dedicated HR Business Partner, and a skills framework
- Flexible working hours
- Remote work
- Meal vouchers
- Mobility allowance
- Services platform
- Commitment to responsible business practices and a strong CSR policy
- Projects with tangible impact for major corporate and mid-sized clients
- Multidisciplinary teams combining Consulting, Design, Data, AI, Cybersecurity, and Technology