Senior Staff Data Engineer

Job not on LinkedIn

🔥 0 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 11%

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Kodiak Solutions

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2005

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

Kodiak Solutions is a healthcare-focused SaaS company that provides a cloud-based Revenue Cycle Analytics (RCA) platform and expert services to hospitals and health systems. The company combines a large clinical and financial data pool with proprietary analytics and in-house specialists to optimize revenue cycle operations, improve net revenue, support government reimbursement and compliance, and automate workflows for finance and risk teams. Kodiak primarily serves enterprise healthcare organizations and finance/revenue cycle leaders with data-driven insights, reporting, and advisory solutions.

📋 Description

• 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

• 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.

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