Data Platform & Analytics Engineer

Altus Fire & Life Safety is a full-service fire and life safety services platform. We offer a complete suite of solutions, from the design, engineering, installation, and servicing of state-of-the-art fire and life safety systems to training, consulting, crisis management and business continuity services.

Altus Fire & Life Safety is accelerating its Data and AI Journey. We are building a modern warehouse with a partner now, and we are looking for a hands-on Data Platform & Analytics Engineer who can help turn that effort into trusted, usable data for the business.
This role is for someone who wants to build the data foundation, not inherit a mature one. You will work with consultants, colleagues, and the executive team to validate core KPIs, build trusted dbt models and data marts, define data-quality rules, and create the semantic layer we need for reporting, analytics, and future AI use cases. The work will be hands-on, sometimes messy, and highly visible.

We value our well-being just as much as our hard work. We are committed to a holistic approach towards benefits plans and programs that support the mental, physical and financial well-being of our team members.

Salary Range: $130,000 - $150,000ResponsibilitiesWhat You Will Do

• Build the Trusted Data Foundation: Develop core dbt models, staging layers, data marts, and reusable business logic in Snowflake so teams can trust the numbers they use to run the business.
• Partner with consultants and Internal Leaders: Be the day-to-day Altus counterpart to our warehouse partner while working directly with executive, branch, and FP&A stakeholders to make sure the data model reflects how the business actually operates.
• Validate Core KPIs: Help turn KPI ideas into definitions people can trust: source of record, grain, numerator, denominator, exclusions, timing rules, ownership, and known limitations.
• Build Data Quality Into the Model: Add tests, reconciliation checks, documentation, and exception reporting so bad data is visible and explainable instead of quietly buried in a dashboard.
• Design for AI-Ready Data: Structure models, dimensions, semantic definitions, and metadata so future AI tools can answer business questions using governed, trusted data rather than ad hoc logic.
• Deliver Practical Business Use Cases: Focus first on branch performance, labor/productivity, sales pipeline, and FP&A needs. The point is not just to move data; it is to help leaders see what is happening and what to do next.

What You Won’t Do

• Operate as a Passive Ticket-Taker: You will not simply receive dashboard requests and write one-off SQL. You will be expected to ask why, clarify definitions, and help shape better data products.
• Own BI Polish Alone: You do not need to be a dashboard wizard. We care more about trusted models, clear metric definitions, and useful decision workflows than highly designed visualizations.
• Run a Large Team on Day One: This is a high-impact individual contributor role. Over time, the right person could grow into a broader data lead role as the function matures.

Our Core Tech Stack

• Warehouse: Snowflake
• Ingestion: Fivetran
• Transformation and Modeling: dbt, SQL
• Analysis and Automation: Python
• BI / Applications: To be determined; early use cases may include lightweight Streamlit apps or other fit-for-purpose reporting layers
• Ways of Working: Git-based development, code review, testing, documentation, and close partnership with business stakeholders

Required Skills

• Experience: 4+ years in analytics engineering, data engineering, BI engineering, or a similar hands-on data role. We care more about judgment and ownership than exact title history.
• SQL and dbt Strength: Strong modern SQL skills and practical experience building, testing, documenting, and maintaining dbt models.
• Business Partnership: Proven ability to work with non-technical leaders, unpack ambiguous requests, and translate business questions into modeled data and repeatable reporting logic.
• Modeling Judgment: Strong understanding of grain, source of record, dimensions, fact tables, marts, semantic layers, and why small definition choices can materially change the number.
• Data Quality Mindset: Strong instincts for testing, reconciliation, exception handling, lineage, and making exclusions visible rather than silent.
• Ownership Mindset: You do not wait for someone else to resolve ambiguity. You identify the open question, find the right owner, document the decision, and move the work forward.
• AI Pragmatism: You are interested in AI as a force multiplier, but you understand that AI is only useful when the underlying data, definitions, and governance are trustworthy.

sponsorship is not available at this time.