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Snowflake is seeking a Senior Data Engineer to design and build the data infrastructure powering internal decision-making and external product capabilities. This role operates across the full data lifecycle—from raw ingestion through transformation, modeling, and consumption—requiring deep technical craft, strong ownership instincts, and the ability to navigate complex system design decisions.
Key responsibilities include designing and launching production-ready data models and pipelines that scale across the enterprise; owning end-to-end system design decisions with clear documentation of architectural tradeoffs; implementing enterprise-grade data governance frameworks and maintaining rigorous data quality standards; developing and optimizing data ingestion processes from diverse sources; and aligning with the product roadmap to build platform tools while serving as an internal customer zero.
You will lead quality assurance efforts by defining testing strategies and identifying risks; build strong stakeholder relationships at all levels, translating ambiguous requirements into well-scoped technical work; and proactively adapt to changing business requirements while maintaining solution integrity. This is not a ticket-execution role—you will help redefine how data engineering work gets done at scale.
Required qualifications include a Bachelor's degree in Computer Science, Information Systems, or related field (or equivalent practical experience) and 5–8 years of hands-on experience building and operating production data pipelines, data models, and platform infrastructure at scale. Expert-level SQL and strong Python skills are essential, with demonstrated experience in performance tuning, query optimization, and schema design for large-scale analytical workloads.
Technical requirements include hands-on experience with Snowflake (Snowpark, dynamic tables, data sharing, Snowflake Cortex, cost optimization); deep expertise in dbt (advanced modeling patterns, testing frameworks, macro authoring, governance); strong proficiency with Apache Airflow (DAG design, operator customization, dependency management); and solid understanding of dimensional modeling, data vault, and semantic layer design.
Preferred skills include experience designing data pipelines and feature engineering for ML model training; exposure to MLflow, feature stores, vector databases, or LLM serving infrastructure; and experience with Snowflake Cortex AI functions or similar LLM API integrations. Desired skills include cloud infrastructure knowledge (AWS, Azure, GCP), streaming ingestion patterns (Kafka, Snowpipe Streaming), and familiarity with data contract frameworks.
Professional skills critical to success: strong system design and architectural reasoning; excellent written and verbal communication; collaborative problem-solving that elevates team practices; self-directed ownership instincts; and ability to navigate ambiguity and distill requirements into well-scoped work.
