Staff Analytics Engineer at Intrado Life & Safety, Inc.

, , United States -

Full Time

Start Date

Immediate

Expiry Date

15 May, 26

Salary

0.0

Posted On

14 Feb, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Architecture, Gold Layer Ownership, Semantic Layer Design, Data Modeling, Advanced SQL, Performance Tuning, Python, Pandas, Power BI, Tableau, Data Visualization Architecture, LLM Application, Dimensional Modeling, Star Schema, Snowflake Schema, Mentorship

Industry

Telecommunications

Description

About Us Intrado is dedicated to saving lives and protecting communities, helping them prepare for, respond to, and recover from critical events. Our cutting-edge company strives to become the most trusted, data-centric emergency services partner by uniting fragmented communications into actionable intelligence for first responders. At Intrado, all of our work truly matters. Responsibilities/Qualifications We are seeking an exceptional Staff Analytics Engineer to transform our raw data into trusted business insights. In this pivotal role, you will partner with a newly hired Staff Data Engineer to architect the end-to-end delivery of our data ecosystem, ensuring that our leadership team has the timely, actionable insights they need to make informed decisions. You will not just build tables; you will own the gold layer, transforming technical complexity into clear, reliable business logic. Your work will directly impact our ability to execute on our strategic plan by ensuring that metrics are defined consistently, trusted, and available quickly to leadership. You will champion a "data as a product" mindset, ensuring that the Business Operations team provides insights that power data-driven decisions across the company. This is a demanding role in a results-oriented environment with high expectations for agency, speed, and ownership. Key Responsibilities End-to-End Data Architecture: You will partner with the Staff Data Engineer to architect the complete data lifecycle. While they build the infrastructure, you will own the gold layer, designing the semantic layer and transforming silver level data in Databricks into optimized fact and dimension tables that serve as the single source of truth for the company. Business Logic & Strategy: Translate complex, ambiguous business questions into concrete technical definitions. You will act as the key technical liaison to build consensus on enterprise-wide metric definitions, ensuring consistent logic across all reports. Data as a Product: Champion a product-oriented mindset by setting the standard for robust documentation, testing, and data model design. Mentorship & Standards: You will lead by example on best practices and code quality to ensure engineering rigor. Performance Tuning: Optimize the connection between Databricks and Power BI, designing models that balance computational load with query latency to ensure dashboards perform. Required Qualifications Experience: 10+ years of relevant, progressive experience in Analytics Engineering, Software Engineering, or Data Engineering, with at least 4 years working in large-scale data lake/warehouse environments (e.g., Databricks). Prior ownership of large-scale data systems and business-critical metrics. Data Modeling Expertise: Mastery of dimensional modeling best practices. You must be an expert in designing modular, reusable, and performant data models (Star Schema, Snowflake Schema) that serve as the foundation for analytics Advanced SQL & Optimization: Proficiency in advanced SQL techniques and performance tuning. LLM Application: Experience leveraging LLMs and AI-assisted development tools to accelerate data engineering workflows, improve code quality, and automate repetitive technical tasks. Intermediate to Advanced Python: Strong expertise in Python for scripting, automation, and data manipulation (Pandas). Data Visualization Architecture: Deep proficiency in Power BI or Tableau, understanding how to architect semantic models that enable polished, high-performance dashboards for executive stakeholders. Business Acumen & Communication: Strong ability to translate technical concepts into business value. You must be able to partner with non-technical stakeholders to define gold level metrics and manage projects across teams. Education: Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a closely related technical field. Preferred Qualifications Master’s or equivalent in Computer Science, Data Science, Analytics or Engineering Prior experience working in a technology company or SaaS environment Total Rewards Want to love where you work? At Intrado, we offer a comprehensive benefits package that includes what you’d expect (medical, dental, vision, life and disability coverage, paid time off, a 401(k) retirement plan, and several that go above and beyond – paid parental leave, access to a robust library of personal and professional training resources, employee discounts, critical illness, hospital indemnity, access to legal support, pet insurance, identity theft protection, an EAP (Employee Assistance Program) that includes free mental health resources/support, and more! Apply today to join us in work worth doing! The starting salary is anticipated between $210,000 and $225,000 and will be commensurate with experience. This position is bonus eligible. Intrado is an Equal Opportunity Employer – Veterans/Disabled and Other Protected Categories. Our Company welcomes and encourages applications of individuals with disabilities. Accommodations are available on request for candidates taking part in all aspects of the selection process. Intrado maintains a Drug Free Workplace.

Responsibilities

The Staff Analytics Engineer will partner with the Data Engineer to architect the end-to-end data lifecycle, owning the gold layer by transforming data into optimized fact and dimension tables that serve as the single source of truth. This role involves translating complex business questions into technical definitions, championing a 'data as a product' mindset, and ensuring consistent, trusted metric definitions across the enterprise.