Senior Staff Data Engineer

Title: Senior Staff Data Engineer Location: Remote

At Kodiak Solutions, we’re dedicated to transforming the healthcare industry through cutting-edge, technology-driven solutions. We specialize in healthcare finance, unclaimed property, risk management, and revenue cycle management, helping healthcare organizations streamline complex financial operations. Our mission is to simplify and optimize processes, so healthcare providers can focus on delivering excellent patient care.

Kodiak Solutions is seeking a hands-on Senior Staff Data Engineer to improve the quality, scalability, and consistency of data engineering across our product portfolio. This individual contributor will lead technically complex initiatives, establish practical engineering standards, and equip delivery teams to build and operate reliable data solutions independently.

The role requires deep production expertise in Snowflake, dbt, SQL, and Python. The successful candidate can move between platform-level technical strategy and implementation: profiling slow queries, improving transformations, building reusable components, and coaching engineers through challenging production problems.

Key Responsibilities

Snowflake performance, scale, and cost

• 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.

dbt standards and development practices

• 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.

Data reliability and operational excellence

• 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.

Modernization and technical leadership

• 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.

Required Qualifications

• 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 Qualifications

• 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.