Senior Staff Data Engineer

Kodiak Solutions

. Lead diagnosis and remediation of Snowflake query, warehouse, concurrency, and workload-performance issues across products.

Posted 11/2/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expert-level proficiency in Snowflake and dbt, focusing on performance tuning, cost optimization, and data quality engineering. Capable of leading cross-team engineering improvements and establishing shared practices while mentoring others in a collaborative environment.

Highest-signal resume keywords

Expert-Level Snowflake ExperienceExtensive Production dbt ExperienceAdvanced SQL SkillsStrong Python Engineering SkillsExperience Operating Large-Scale Cloud Data Platforms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Snowflake Query ProfilingWarehouse OperationsPerformance TuningCost OptimizationDbt Model DesignIncremental ProcessingAutomated TestingCI/CD PracticesAdvanced SQLPython Engineering

Soft Skills

Clear CommunicationTechnical JudgmentMentorship

Tools & Technologies

Microsoft AzureAzure Data FactoryAzure Data Lake StorageAzure DevOpsDatadogObservability Tooling

Industry Keywords

ELTData ModelingPipeline ReliabilityData Quality EngineeringSensitive-Data ControlsRole-Based AccessGovernanceHealthcareFinancial ProcessingData-Intensive SaaS Products

Tech Stack

Tools & technologies

AzureCloudPythonSnowflakeSQL

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 measurable workload and consumption data.
Partner with product teams to address performance risks before production incidents.
Define and implement 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, clear ownership, lineage, and appropriately scoped 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 engineering fixes.
Introduce automated performance, correctness, and production-readiness checks for critical workloads.
Guide modernization of legacy SQL, stored procedures, and tightly coupled processing into maintainable data architectures.
Translate enterprise architecture guidance into implementable design patterns and coding practices.
Advise teams on orchestration, modeling, ingestion, and integration tradeoffs.
Lead cross-team engineering improvements through hands-on delivery, design reviews, and mentoring.
Partner with engineering leaders to identify and sequence technical debt with measurable operational outcomes.

Requirements

What you’ll need

10+ years of software or data engineering experience, including substantial technical leadership across teams.
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, and Azure DevOps.
Preferred: Experience migrating legacy SQL Server or stored-procedure-heavy environments.
Preferred: Experience with Datadog, observability tooling, infrastructure as code, and developer-enablement automation.
Preferred: Knowledge of sensitive-data controls, role-based access, lineage, and governance in regulated environments.
Preferred: Background in healthcare, complex financial processing, or other data-intensive SaaS products.