Senior Data Engineer

Senior Data Engineer Overview:

We are seeking an experienced, hands-on Senior Data Engineer to lead the design, architecture, and population of our new BI data warehouse. This role is highly cross-functional and critical to Incident IQ's next phase of growth. You will be responsible for aggregating complex data streams from our core SaaS platform and third-party systems into a centralized data warehouse/lake. You will own the analytics foundation end-to-end, serving as an architect-engineer. This is a genuine greenfield seat: the analytics foundation you build becomes how the company and its customers answer their most important questions. 

Key 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.

Qualifications & Experience

• Experience: 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.
• 0-to-1 Build Experience: You have owned a greenfield data infrastructure initiative end to end rather than contributed to one. Expect to name it and walk through what you built, what you chose against, and what you would do differently.
• Data Warehousing: Deep expertise in modern cloud data warehousing solutions (e.g., Snowflake, Databricks, Azure Synapse, or AWS Redshift) and data lake architectures.
• Pipeline Development: Proven experience building complex ETL/ELT pipelines using tools like dbt, Apache Airflow, Azure Data Factory, or similar orchestration frameworks.
• Database Mastery: Advanced proficiency in complex SQL and query tuning across relational sources (Azure SQL, SQL Server, PostgreSQL). Experience integrating a non-relational source such as MongoDB into a relational warehouse is a plus rather than a requirement. 
• SaaS Integrations: Hands-on experience extracting and integrating data from enterprise GTM tools such as Salesforce, Gainsight, Marketo, or similar platforms.
• Programming: Proficiency in languages such as Python, Scala, or C# for data manipulation and automation.

Personality & Soft Skills

• 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.

What This Role Is Not

• Not Just a Dashboard Developer: While reporting is key, this is a heavy data engineering and architecture role. If you prefer to only build visualizations without touching the underlying pipeline and warehouse design, this is not the right fit.
• Not a Maintenance Role: We are building a new data ecosystem from the ground up. This role requires someone who thrives on creating scalable 0-to-1 solutions rather than maintaining legacy systems.
• Not an Order Taker: You will be expected to advise leadership on data strategy and best practices, not simply execute assigned tickets.