Senior Data Engineer
Build and operate scalable enterprise data pipelines, models, and platform infrastructure across the full data lifecycle. The role requires 5+ years of experience, strong SQL and Python, and deep expertise in Snowflake, dbt, and Airflow.
About the job
Responsibilities
- Design, build, and launch production-ready data models and pipelines across ingestion, transformation, modeling, and consumption.
- Own end-to-end system design decisions, evaluate tradeoffs, and document architectural choices.
- Implement data governance frameworks and maintain rigorous data quality standards.
- Develop and optimize ingestion processes from diverse enterprise sources.
- Build data platform tools aligned with the product roadmap and provide internal customer feedback.
- Apply Agile practices to data product development, including sprint planning, reviews, and retrospectives.
- Define testing strategies, identify risks, and drive resolution of technical issues.
- Translate ambiguous stakeholder requirements into well-scoped technical work.
- Adapt solutions to changing business requirements while preserving technical integrity.
Requirements
- Bachelor's degree in Computer Science, Information Systems, or a related field, or equivalent practical experience.
- 5–8 years of experience building and operating production data pipelines, data models, and platform infrastructure at scale.
- Expert SQL and strong Python skills, including performance tuning, query optimization, and schema design.
- Hands-on Snowflake experience, including Snowpark, dynamic tables, data sharing, Snowflake Cortex, and cost optimization.
- Advanced dbt modeling, testing, macro authoring, and project governance on Snowflake.
- Strong Apache Airflow experience, including DAG design, operator customization, dependency management, and operations.
- Understanding of dimensional modeling, data vault, and semantic layer design.
- Strong system design, architectural reasoning, communication, collaboration, ownership, and ambiguity-management skills.
Preferred Qualifications
- Experience designing data pipelines and feature-engineering workflows for ML training and inference.
- Exposure to MLflow, feature stores, vector databases, or LLM serving infrastructure.
- Experience with Snowflake Cortex AI functions or similar LLM API integrations.
- Cloud infrastructure experience with AWS, Azure, or Google Cloud, including infrastructure as code and cost management.
- Knowledge of Kafka, Snowpipe Streaming, and real-time data architectures.
- Familiarity with data contracts and schema registry tooling.
Skills
SQL, Python, Snowflake, Snowpark, Dynamic Tables, Snowflake Cortex, dbt, Apache Airflow, Dimensional Modeling, Data Vault, Semantic Layer, MLflow, Kafka, Snowpipe Streaming, Infrastructure As Code
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