Staff Data Engineer
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Job Description
Staff Data Engineer improving Snowflake and dbt reliability, performance, and standards. Modernizing data platforms for Kodiak Solutions’ healthcare finance and revenue-cycle products.
Responsibilities:
- 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 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 independent team adoption
- 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 for recurring data-processing incidents and prioritize durable 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 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:
- 8+ 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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