Senior Data Engineer

We are seeking a seasoned Senior Data Engineer to drive scale, performance, and actionability within our data ecosystem. You will strengthen and scale our Python and Snowflake -based infrastructure, building the core capabilities required to support dynamic analytics and our transition toward more automated, self-service data workflows.

We need an engineer who can adapt to a dynamic product environment, who can ask the ‘why’ behind the ‘what’ to ensure we are building the right solutions.

Key Responsibilities

• Data Enablement: Support the analytics layer by developing core Looker views and building out our agentic AI infrastructure to derive automated self-service capabilities and reduce reporting bottlenecks.
• Pipeline Architecture: Design, build, and scale ELT pipelines that are resilient, efficient, and modular.
• Cross-Functional Collaboration: Partner with Finance, Product, and Analytics to ensure our data models solve the right problems.
• Data Modeling: Build and maintain analytics schemas (including Star Schemas ) that abstract complex logic into user-friendly datasets.
• End-to-End Ownership: Lead projects from inception to production, taking accountability for data integrity and the trustworthiness of the platform.
• Operational Excellence: Monitor production health using monitoring tools (e.g. Datadog , Monte Carlo ) ensuring our data ecosystem remains robust and reliable.
• Technical Leadership: Act as a technical lead, conduct code reviews, define engineering culture, and champion best practices.

Requirements

• Experience: 5+ years in data engineering, with at least 2+ years focused on distributed, large-scale cloud data warehouses.
• Snowflake Expertise: Proven experience with Snowflake performance optimization and cost-governance. Familiarity with Snowflake Cortex/MCP is a plus.
• Technical Mastery:
  • Advanced Software Engineering (Python): Deep proficiency in Python , with a focus on writing modular, reusable, and testable code (unit/integration tests) for complex data processing.
  • Data Modeling & SQL Mastery: Expert-level SQL and a sophisticated understanding of data warehousing methodologies to build performant, scalable analytics layers.
  • Infrastructure & Automation: Practical experience with modern CI/CD frameworks (e.g., GitHub Actions , Argo CD ) to drive engineering velocity and platform stability.
  • Workflow Orchestration: Expertise in architecting and scaling orchestration-as-code workflows (e.g., Prefect or Airflow ) to manage complex dependencies and ensure pipeline resilience.
  • Observability & Reliability: Deep proficiency in deploying monitoring and alerting frameworks (e.g., Datadog ) to maximize system uptime while mitigating alert fatigue.
• Operational Maturity: Experience managing business-critical production pipelines with a focus on uptime, data quality, and defining SLAs.
• Education: BS/MS in Computer Science, Information Systems, or a related technical field.