Youll be part of a data-driven organization where every voice matters, working at the heart of our BI team to raise the bar for analytical excellence.
In this role, youll take end-to-end ownership of complex, high-impact BI initiatives, shaping how measures success, makes decisions, and scales. Your work will directly impact over 1M users worldwide, empowering B2B sales teams to unlock new revenue opportunities and drive sustainable growth.
What Youll Actually Do:
Lead the design and evolution of BI data foundations, owning key product and GTM metric definitions and data models
Build, scale, and maintain high-quality ELT pipelines and curated datasets (raw → modeled → semantic) that power dashboards and self-serve analytics
Collaborate closely with Data Scientists to operationalize machine learning use cases, including feature pipelines and analytical datasets
Take senior ownership within the BI team by mentoring peers, reviewing critical work, and promoting best practices in analytics engineering and semantic modeling
Own the performance, cost, and reliability of the data warehouse and BI layer by optimizing models, queries, and incremental processing patterns
Translate ambiguous business questions into clear, governed metrics and scalable data models, partnering with Product, RevOps/GTM, Finance, and Analytics
Establish and maintain strong data quality, observability, and monitoring practices (tests, SLAs, anomaly detection)
Drive standardization and automation across the BI development lifecycle, including version control, CI/CD, documentation, and release processes
Stay ahead of modern BI and analytics engineering trends, including AI-assisted development, and apply them pragmatically to increase trust and speed
5+ years of experience in Data Engineering / BI roles, with proven ownership of scalable, end-to-end data ecosystems
Expert-level experience with modern data stacks, including Snowflake or Databricks and dbt as a core transformation layer
Advanced SQL and Python skills, with hands-on responsibility for CI/CD pipelines, data quality frameworks, and observability
Deep understanding of dimensional modeling, data warehousing patterns, and semantic layer design
Hands-on experience with orchestration tools such as Airflow, managing complex and high-availability ELT workflows
Experience working with large-scale data processing, including exposure to Kafka, Spark, or streaming architectures
Strong ability to independently lead cross-functional initiatives and translate business requirements into executable technical solutions
Strong business and analytical mindset, with an AI-forward approach and curiosity for emerging tools and methodologies
