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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and maintaining dbt models and Snowflake applications, with a strong focus on data quality, performance optimization, and semantic layer design for BI and AI consumers. Proficient in SQL, Python, and data engineering best practices, ensuring effective collaboration with stakeholders and adherence to coding standards.
Highest-signal resume keywords
Dbt Model DevelopmentSnowflake Platform ExpertiseSQL OptimizationData EngineeringDimensional Modeling
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
DbtSnowflakeSQLPythonData ModelingData ProcessingIncremental ModelsJinjaGitHubSemantic Layer Design
Soft Skills
CollaborationProblem-SolvingCommunication
Tools & Technologies
StreamlitGitHubSnowpipeTasksDynamic Tables
Certifications & Qualifications
Dbt Analytics Engineering CertificationSnowflake SnowPro CoreSnowflake Advanced ArchitectSnowflake Data Engineer
Industry Keywords
Data EngineeringAnalytics EngineeringMedallion ArchitectureData Quality FrameworksETL/ELT
Tech Stack
Tools & technologiesApacheCloudETLMicroservicesPythonSQL
About the role
Key responsibilities & impact- Develop, test, and maintain dbt models across silver, gold, and platinum medallion layers
- Extend the production dbt project and migrate legacy transformation logic into dbt
- Build and maintain semantic-layer models for BI, integration, and AI consumers
- Implement incremental models, snapshots, and Snowflake-optimized materialization strategies
- Author dbt tests and enforce layer contracts, naming conventions, and coding standards
- Maintain dbt documentation, lineage, and exposures
- Develop reusable macros and packages
- Participate in pull request reviews and follow GitHub branching and release processes
- Troubleshoot dbt model failures, data quality issues, and performance bottlenecks
- Partner with BI analysts and business stakeholders to translate reporting requirements into dbt models
- Design and develop interactive Snowflake Streamlit data applications
- Build reusable application frameworks and component libraries
- Integrate Snowflake Native Apps and third-party marketplace applications
- Develop custom UDFs and stored procedures
- Implement data models and pipelines following medallion architecture standards
- Process JSON, Parquet, and Avro into consumable structures
- Optimize query performance, clustering, and warehouse usage
- Implement secure data sharing solutions
- Build semantic models, metrics, business definitions, semantic views, and AI-ready datasets
- Collaborate with AI engineering teams and validate AI-generated answers against governed metrics
Requirements
What you’ll need- Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related technical field
- Minimum of 3 years of experience in data engineering, analytics engineering, or data development roles
- At least 2 years of hands-on experience developing and maintaining dbt projects in production
- At least 2 years of hands-on experience with the Snowflake platform, including advanced features
- Proven track record of developing data applications or analytical solutions for business users
- Production experience with dbt, including model design, Jinja and macros, testing, packages, documentation, and deployment on Snowflake
- Strong dimensional modeling skills and experience designing layered transformations
- Experience defining metrics, dimensions, and business logic in a semantic layer for BI and AI consumption
- Advanced knowledge of Snowflake architecture, including data warehousing, data lakes, and lakehouse features
- Expert-level SQL skills with complex analytical queries and optimization
- Proficiency with Snowflake Streamlit
- Strong Python skills for Snowpark development, data processing, and application logic
- Experience with GitHub, pull request workflows, and branching strategies
- Experience migrating legacy ETL/ELT transformation logic into dbt
- Experience with dbt incremental strategies, snapshots, and model contracts on Snowflake
- Understanding of the dbt Semantic Layer (MetricFlow) and Snowflake semantic views
- Familiarity with semantic models and metadata supporting LLM-based tools such as Snowflake Cortex Analyst
- Knowledge of medallion architecture patterns and data quality frameworks
- Understanding of open table formats, including Apache Iceberg, Delta Lake, and Apache Hudi, and integration with Snowflake
- Experience processing semi-structured data, including JSON, Parquet, and Avro, in Snowflake
- Familiarity with Snowflake Dynamic Tables, Snowpipe, Tasks, and Streams
- Understanding of data sharing, marketplace solutions, and cross-cloud data collaboration
- Must be currently authorized to work in the applicable country full-time without employer-sponsored immigration support now or in the future
- Preferred: dbt Analytics Engineering Certification; Snowflake SnowPro Core, Advanced Architect, or Data Engineer certification
- Preferred: dbt packages such as dbt_utils and dbt_expectations, dbt Cloud or dbt Core orchestration, and data observability tools
- Preferred: AI/ML collaboration, modern web frameworks, API development, microservices, cloud data services, and data governance
Benefits
Comp & perks- Competitive compensation with performance-based incentives
- Career development programs and internal promotion pathways—we build from within
- Tuition reimbursement and continuous learning opportunities
- Comprehensive health, dental, and vision coverage
- Retirement savings plans with company match
- Life insurance and disability coverage
- Paid time off and holidays
- Flexible work arrangements where applicable
- Employee Assistance Programs (EAP) and wellness resources
- Inclusive, team-oriented culture
- Products contributing to a safer, more sustainable built environment