Lead BI Engineer, Altana

• 7+ years of professional experience in analytics engineering, BI engineering, or a closely related data discipline
• Deep fluency in SQL and at least one transformation framework (dbt strongly preferred)
• Strong dimensional modeling skills: star/snowflake schemas, conformed dimensions, incremental loading patterns
• Production experience operating a modern lakehouse stack (e.g., Snowflake/BigQuery/ Databricks), including transformation tooling (dbt or DLT) and a BI layer (Looker, Tableau, or Sigma)
• Track record of defining and shipping company-wide KPI frameworks and executive dashboards
• Comfort working in ambiguity: you have built a BI function (or a significant new workstream within one) from scratch before
• Excellent communication skills—you can explain a data model to an engineer and a retention curve to a CRO with equal clarity

Desirable

• You will be the founding BI Engineer at Altana, responsible for building the analytical infrastructure that enables data-driven decision-making across the entire company
• This is not a reporting role that sits downstream of decisions already made—it is a foundational engineering role that will define how Altana understands its own business, customers, and products
• You will own the full stack from data modeling and pipeline orchestration through semantic layers, dashboards, and self-service tooling
• You will partner directly with engineering, product, go-to-market, and finance leadership to translate ambiguous business questions into reliable, well-modeled data products
• Because you are the first hire in this function, you will also set the technical direction, tooling choices, and quality standards that future BI engineers will inherit
• You'll design and implement a scalable analytics data warehouse (or lakehouse) layer on top of Altana’s existing data infrastructure
• Define the canonical dimensional model: facts, dimensions, conformed keys, and slowly changing dimensions for core business entities (customers, transactions, subscriptions, product usage, pipeline)
• Stand up and own the transformation layer (e.g., dbt) with version control, CI/CD testing, and documentation standards from day one
• Establish a semantic/metrics layer so that “revenue,” “active customer,” and “retention” mean exactly one thing company-wide
• Build and maintain executive dashboards covering SaaS metrics (ARR, NRR, GRR, expansion/contraction, logo churn), product engagement, and operational health
• Create self-service datasets and exploration environments so that PMs, GTM leads, and finance can answer their own questions without filing a ticket
• Own data quality monitoring, alerting, and incident response for the BI layer
• Act as the embedded analytics thought partner for engineering leadership, product, sales, CS, and finance—translating business context into data requirements and vice versa
• Proactively identify trends, anomalies, and opportunities in the data; surface them before anyone asks
• Drive alignment on KPI definitions, measurement methodology, and reporting cadences
• Evaluate and recommend the BI tooling stack (visualization, orchestration, cataloging, observability) with an eye toward total cost of ownership and long-term maintainability while re-using infrastructure and tooling already part of the larger stack
• Write the playbook: style guides, naming conventions, PR review standards, and on-call expectations for the BI function
• Hire, mentor, and grow the BI team as demand scales