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.

Responsibilities

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; Design, build, and scale ELT pipelines that are resilient, efficient, and modular; Partner with Finance, Product, and Analytics to ensure our data models solve the right problems; Build and maintain analytics schemas (including Star Schemas) that abstract complex logic into user-friendly datasets; Lead projects from inception to production, taking accountability for data integrity and the trustworthiness of the platform; Monitor production health using monitoring tools (e.g. Datadog, Monte Carlo) ensuring our data ecosystem remains robust and reliable; Act as a technical lead, conduct code reviews, define engineering culture, and champion best practices

Requirements

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

Nice to have

Familiarity with Snowflake Cortex/MCP is a plus

Benefits

comprehensive benefits package; annual bonus program