Senior Data Engineer

Incident IQ is seeking a Senior Data Engineer to build and own its analytics foundation, including a new BI data warehouse and data lake. The role will integrate data from the SaaS platform and third-party systems, establish data quality and governance practices, and ensure reliable data freshness and performance.

Responsibilities:

• Data Warehouse Architecture: Design, implement, and scale a modern BI data warehouse/data lake from the ground up. Foundation first — a rudimentary, queryable environment stood up early and built out over time, rather than a spreadsheet-first interim. You will design at a grain that serves internal analytics today and groundwork for future customer-facing analytics
• Data Pipeline Engineering: Build and maintain robust, automated ETL/ELT pipelines to extract and aggregate data from diverse sources, beginning with our Azure-hosted product platform and extending to operational systems (e.g., CRM) and other external APIs as product usage pipelines stabilize
• Platform Evaluation and Recommendation: Own the written platform recommendation scored on refresh latency, aggregation performance at full volume, schema evolution, tenant isolation, three-year total cost of ownership, and migration cost. You make the case; selection and purchase are joint decisions with R&D and Finance
• Data Quality & Governance: Establish and enforce data governance, quality checks, and performance optimization practices to ensure high data integrity and rapid query response times. You will own source-system semantics and technical implementation
• Reliability and Continuity: Own freshness, completeness, and reconciliation against a published refresh SLA. Document to the standard that others can run the full cycle unaided

Requirements:

• Minimum of 5-7 years of professional experience in Data Engineering, BI Engineering, Analytics Engineering, or Data Architecture, specifically within a high-growth SaaS environment
• You have owned a greenfield data infrastructure initiative end to end rather than contributed to one
• Deep expertise in modern cloud data warehousing solutions (e.g., Snowflake, Databricks, Azure Synapse, or AWS Redshift) and data lake architectures
• Proven experience building complex ETL/ELT pipelines using tools like dbt, Apache Airflow, Azure Data Factory, or similar orchestration frameworks
• Advanced proficiency in complex SQL and query tuning across relational sources (Azure SQL, SQL Server, PostgreSQL)
• Hands-on experience extracting and integrating data from enterprise GTM tools such as Salesforce, Gainsight, Marketo, or similar platforms
• Proficiency in languages such as Python, Scala, or C# for data manipulation and automation
• Strategic Builder: You don't just write scripts; you think holistically about how data architecture drives business outcomes and product differentiation
• Pragmatic and Cost-Conscious: You are comfortable standing up something rudimentary that works and improving it, rather than waiting on the ideal platform. You weigh cost against capability and can defend the tradeoff to a non-technical audience
• Cross-Functional Communicator: You can translate complex technical data models into plain business language for stakeholders, and vice versa
• Autonomous Ownership: You thrive in environments where you can take a high-level goal, determine the technical path, and execute with minimal hand-holding
• Inquisitive Problem Solver: You are naturally curious about how the business operates and how data can be used to uncover hidden efficiencies
• Collaborative Team Player: Focused on the success of the platform and the company over personal credit
• Experience integrating a non-relational source such as MongoDB into a relational warehouse is a plus rather than a requirement