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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and maintaining data pipelines using Databricks, Azure Data Lake, and Python/PySpark, while ensuring data quality and operational efficiency. Proficient in ETL/ELT processes, data integration, and collaboration with cross-functional teams to deliver trusted data products.
Highest-signal resume keywords
Data Pipeline DevelopmentETL/ELT ProcessesDatabricks ProficiencySQL and Python/PySparkAzure DevOps Experience
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Build, test, deploy, and support data pipelines and curated datasets using Databricks and Azure Data Lake
- Build and maintain ETL/ELT pipelines using Databricks, Python/PySpark, SQL, Delta Lake, and Azure services
- Ingest data from databases, APIs, files, SaaS applications, and event sources using batch or streaming patterns
- Develop bronze, silver, and gold datasets using reusable and configuration-driven engineering approaches
- Implement incremental processing, schema handling, error handling, restorability, reconciliation, and audit controls
- Apply data-quality rules, testing, lineage, and monitoring to support trusted data products
- Work with architects, analysts, and business teams on source-to-target mappings, business rules, and acceptance criteria
- Create curated datasets for Power BI, enterprise reporting, analytics, data science, AI, and downstream applications
- Tune SQL, Spark workloads, storage, and table design with guidance from senior team members
- Use Git and Azure DevOps for user stories, branching, pull requests, CI/CD, releases, and traceability
- Create unit, integration, and reconciliation tests and participate in design and code reviews
- Monitor production pipelines, resolve incidents, support root-cause analysis, and document operational procedures
- Contribute reusable components, documentation, and continuous improvements across the DCoE data team
Requirements
What you’ll need- 2+ years of hands-on data engineering or software engineering experience with data pipelines and data transformations
- Practical experience with SQL and Python or PySpark
- Working knowledge of Databricks, Azure Data Lake, Delta Lake, or comparable cloud data technologies
- Understanding of ETL/ELT, data integration, data-quality checks, and lakehouse or data-warehouse concepts
- Familiarity with Git, CI/CD, Agile delivery, and Azure DevOps or a comparable delivery tool
- Ability to diagnose data and pipeline issues and work effectively with technical and business stakeholders
- Bachelor’s degree in computer science, information systems, engineering, or a related field, or equivalent practical experience
- Databricks or Microsoft Azure certification preferred
- Experience with Power BI consumption patterns, Unity Catalog, streaming, or metadata-driven frameworks preferred
- Experience with supply chain, manufacturing, ERP, operations, or industrial datasets preferred
- Strong analytical and structured problem-solving skills
- Clear communication across technical and business audiences
- A collaborative, ownership-focused approach to delivery
- Ability to balance quality, speed, maintainability, and operational needs
- Curiosity and willingness to learn from senior engineers, architects, analysts, and domain experts
Benefits
Comp & perks- Health care
- Dental coverage
- Vision plans
- 401K benefits
- Commissions/incentive compensation depending on the role
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 PipelineETLELTPythonPySparkSQLDatabricksAzure Data LakeDelta LakeData Integration
Soft Skills
Analytical SkillsProblem-SolvingClear CommunicationCollaborationOwnership-Focused Approach
Tools & Technologies
GitAzure DevOpsPower BICI/CDAgile Delivery
Certifications & Qualifications
Databricks CertificationMicrosoft Azure Certification
Industry Keywords
Data Quality ChecksLakehouseData WarehouseSupply ChainManufacturingERPOperationsIndustrial Datasets