Staff Data Engineer

Kodiak Solutions is seeking a hands-on Staff Data Engineer to improve data quality, scalability, and consistency across its product portfolio. The role leads complex Snowflake and dbt initiatives, establishes engineering standards, and helps teams build and operate reliable data solutions.

Responsibilities:

• Lead diagnosis and remediation of Snowflake query, warehouse, concurrency, and workload-performance issues across products
• Establish practical 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 they become 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 that teams can adopt without relying on centralized approvals
• Establish repeatable approaches to data quality, reconciliation, observability, incident triage, and pipeline recovery
• Improve monitoring and actionable alerting across Snowflake, dbt, Azure services, and Datadog in partnership with SRE and application teams
• Lead root-cause investigations for recurring data-processing incidents 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 based on workload requirements
• Lead high-impact, 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:

• 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
• Experience with Microsoft Azure, Azure Data Factory, Azure Data Lake Storage, and Azure DevOps
• Experience migrating legacy SQL Server or stored-procedure-heavy environments
• Experience with Datadog, observability tooling, infrastructure as code, and developer-enablement automation
• Knowledge of sensitive-data controls, role-based access, lineage, and governance in regulated environments
• Background in healthcare, complex financial processing, or other data-intensive SaaS products