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Staff Data Engineer
Kodiak Solutions. Lead diagnosis and remediation of Snowflake query, warehouse, concurrency, and workload-performance issues across products .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expert-level proficiency in Snowflake query optimization, dbt model design, and advanced SQL and Python engineering. Capable of leading cross-team engineering improvements and establishing shared engineering practices while ensuring data quality and performance.
Highest-signal resume keywords
Expert-Level Snowflake ExperienceExtensive Production dbt ExperienceAdvanced SQL SkillsStrong Python Engineering SkillsExperience Operating Large-Scale Cloud Data Platforms
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Query ProfilingWarehouse OperationsPerformance TuningCost OptimizationModel DesignIncremental ProcessingAutomated TestsData ModelingPipeline ReliabilityData Quality Engineering
Soft Skills
Clear CommunicationMentorshipTechnical Judgment
Tools & Technologies
Microsoft AzureAzure Data FactoryAzure Data Lake StorageAzure DevOpsDatadogInfrastructure as CodeObservability Tooling
Industry Keywords
HealthcareComplex Financial ProcessingData-Intensive SaaS Products
Tech Stack
Tools & technologiesAzureCloudPythonSnowflakeSQL
About the role
Key responsibilities & impact- Lead diagnosis and remediation of Snowflake query, warehouse, concurrency, and workload-performance issues across products
- Establish approaches to workload isolation, warehouse sizing, query optimization, performance benchmarking, and capacity planning
- Build cost-visibility and optimization practices using workload and consumption data
- Partner with product teams to address performance risks before production incidents
- Define reusable dbt project conventions, materialization patterns, macros, packages, and documentation
- Standardize model testing, incremental transformations, version control, pull-request checks, and CI/CD quality gates
- Promote maintainable transformation design, ownership, lineage, and data contracts
- Create reference implementations and templates for delivery teams
- Establish approaches to data quality, reconciliation, observability, incident triage, and pipeline recovery
- Improve monitoring and actionable alerting across Snowflake, dbt, Azure services, and Datadog with SRE and application teams
- Lead root-cause investigations and prioritize durable fixes for recurring data-processing incidents
- Introduce automated performance, correctness, and production-readiness checks
- Guide modernization of legacy SQL, stored procedures, and tightly coupled processing
- Translate enterprise architecture guidance into implementable design patterns and coding practices
- Advise on orchestration, modeling, ingestion, and integration tradeoffs
- Lead cross-team engineering improvements through delivery, design reviews, and mentoring
- Partner with engineering leaders to sequence technical debt with measurable operational outcomes
Requirements
What you’ll need- 8+ years of software or data engineering experience
- Expert-level, hands-on Snowflake experience with query profiling, warehouse operations, performance tuning, and cost optimization
- Extensive production dbt experience with model design, incremental processing, macros, automated tests, and CI/CD
- Advanced SQL and strong Python engineering skills, including building reusable tools and production data workflows
- Experience operating large-scale cloud data platforms and debugging high-volume or concurrent workloads
- Strong grounding in ELT, data modeling, testing, pipeline reliability, and data quality engineering
- Demonstrated success establishing shared engineering practices without becoming a delivery bottleneck
- Ability to influence through technical judgment, clear communication, code reviews, and mentorship
- Preferred: Experience with Microsoft Azure, Azure Data Factory, Azure Data Lake Storage, Azure DevOps, legacy SQL Server migration, Datadog, observability tooling, infrastructure as code, developer-enablement automation, sensitive-data controls, role-based access, lineage, governance, healthcare, complex financial processing, or data-intensive SaaS products