FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in architecting and implementing end-to-end data solutions using Azure Databricks, PySpark, and Azure Data Factory, while optimizing data pipelines and models for performance and scalability. Proficient in collaborating with cross-functional teams to align data engineering efforts with organizational goals and regulatory standards.
Highest-signal resume keywords
Azure DatabricksPySparkAzure Data FactorySQLData Warehouse
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 Pipeline DevelopmentData ModelingPerformance OptimizationREST/JSON API IntegrationAutomated Testing FrameworksData VisualizationData CurationCI/CD AutomationMetadata ManagementData Integration Automation
Soft Skills
Excellent Communication SkillsTeam CollaborationIndependent WorkDeadline Management
Tools & Technologies
Azure SQL ServerPower BIAzure DevOpsGitADLS Gen2
Industry Keywords
GxPGDPRHIPAACFRCTRCTD
Tech Stack
Tools & technologiesAzurePySparkSparkSQLTableauTerraform
About the role
Key responsibilities & impact- Architect, design, and implement end-to-end data solutions using Azure Databricks, PySpark, Azure Data Factory, and Azure SQL
- Design, build, and maintain data pipelines from data sources through integration to consumption
- Implement data modeling standards across bronze, silver, and gold data lake layers
- Develop conceptual, logical, and physical data models
- Optimize Spark and SQL workloads for performance, scalability, and cost efficiency
- Manage metadata using data preparation, integration, and AI-enabled tools
- Drive automation of data integration and repeatable data preparation tasks
- Build REST/JSON API integrations and real-time ingestion frameworks
- Automate workflows using Azure DevOps pipelines and Git-based CI/CD
- Implement reusable pipeline templates for ingestion and transformation
- Develop automated unit, regression, and integration testing frameworks
- Prepare and curate datasets for BI, reporting, and advanced analytics
- Partner with analytics teams to define semantic models and KPIs
- Implement performance-optimized models for self-service analytics
- Support end users with data visualization solutions when needed
- Lead technical design reviews and mentor junior engineers
- Gather and refine data requirements with cross-functional groups, business analysts, and stakeholders
- Collaborate with business and IT stakeholders to align data engineering with organizational objectives
- Contribute to architectural roadmaps and technology evaluations
- Identify inefficiencies and recommend improvements to the executive team
Requirements
What you’ll need- Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field
- 5–8 years of experience designing and developing enterprise-scale data solutions
- Expert-level proficiency in Databricks, Azure Fabric, PySpark, SQL, and Azure DevOps
- Proven experience with Azure Data Factory, ADLS Gen2, and Azure SQL Server
- Strong experience with Microsoft Azure data management architectures, including Data Warehouse, Data Lake, and Data Catalogue
- Experience with Power BI required; Tableau or Looker a plus
- Working knowledge of CI/CD automation, Git, and infrastructure as code (ARM, Bicep, or Terraform)
- Good understanding of GxP, GDPR/HIPAA, and applicable CFR/CTR/CTD regulations
- Demonstrated success working with IT and business stakeholders while integrating analytics and data science output into business processes and workflows
- Excellent written and verbal communication skills
- Ability to work independently and as part of a team and meet important deadlines