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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and maintaining ETL/ELT pipelines across multi-cloud environments, with a strong focus on Snowflake architecture and data orchestration. Proficient in Python, SQL, and PySpark, with a solid understanding of machine learning data needs and generative AI workflows.
Highest-signal resume keywords
Snowflake ArchitectureAWS Data ServicesAzure Data FactoryPython ProgrammingData Engineering Experience
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Design, build, and maintain resilient ETL/ELT pipelines from on-premise systems, AWS services, and Azure platforms into Snowflake and downstream AI services
- Develop and maintain feature stores and analytically optimized datasets for machine learning workflows
- Engineer generative AI data pipelines, including extraction, transformation, chunking, and loading into vector databases
- Implement Snowflake data modeling, Snowpipe automation, and compute and storage optimization
- Execute non-invasive data extraction from legacy systems while preserving system stability
- Design and manage cross-platform workflows using Airflow, AWS Step Functions, and Azure Data Factory
- Partner with IT, database, infrastructure, and security teams on connectivity, access, and production approvals
- Implement automated data quality, validation, and observability frameworks
- Optimize storage, compute usage, and query performance across Snowflake, AWS, and Azure
- Partner with MLOps, Data Science, and AI teams to translate experimental use cases into scalable production-ready data solutions
- Perform other duties as assigned
Requirements
What you’ll need- Master’s degree in Computer Science, Data Engineering, or a related field from an accredited college or university preferred
- Six (6) years of hands-on data engineering experience, with a track record of building production-grade pipelines for Data Science and AI in multi-cloud environments or equivalent combination of education and experience required
- Expert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and integration with external cloud AI services
- Advanced, hands-on knowledge of AWS (S3, Glue, Lambda) and Azure (Data Factory, Synapse) data services
- Mastery of Python, SQL, and PySpark
- Deep experience with data orchestration and containerization (Docker)
- Proven ability to interface with on-premise SQL, mainframe extracts, and flat files and transform them for modern cloud consumption
- Strong understanding of data needs for Machine Learning, including feature engineering
- Strong understanding of Generative AI, including vectorization and embedding pipelines
- Ability to work in a team environment
- Ability to meet or exceed Performance Competencies
- Travel as required
Benefits
Comp & perks- Work-life balance
- Reasonable accommodations when applicable and appropriate
- Equal Opportunity Employer
- Drug-Free Workplace
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
ETL/ELT Pipeline DevelopmentData ModelingData Quality AutomationFeature EngineeringGenerative AIData OrchestrationSQLPySparkPerformance TuningContainerization
Soft Skills
Team CollaborationProblem Solving
Tools & Technologies
AWS (S3, Glue, Lambda)Azure (Data Factory, Synapse)SnowpipeAirflowDocker
Certifications & Qualifications
Master’s Degree in Computer ScienceMaster’s Degree in Data Engineering
Industry Keywords
Data ScienceAI ServicesMulti-Cloud EnvironmentsData ExtractionData Transformation