Analytics Engineer -- Data & Business Intelligence

Job Description

Job Description

Jay Staffing Partners is conducting a confidential search for a high-growth technology company seeking an Analytics Engineer to strengthen the data foundation supporting its products and operations.

This is a hands-on position for someone who enjoys turning complex production data into reliable, scalable, and understandable data models.

You'll take ownership of important data infrastructure, improve the quality and reliability of existing pipelines, and work closely with engineering, product, and operations teams. Your work will support current analytics and reporting needs while establishing a stronger foundation for future data-driven and AI-enabled products.

This role is well suited for someone who has operated in a fast-moving technology environment and can demonstrate clear ownership and measurable impact from their previous work.

What You'll Own

• Design, build, and maintain production data models and pipelines.
• Improve the reliability, scalability, and usability of existing data infrastructure.
• Transform complex production data into trusted datasets for reporting, analytics, and product use cases.
• Support data requirements associated with increasingly sophisticated customer implementations.
• Partner with product and operations teams to understand business requirements and translate them into scalable data solutions.
• Collaborate directly with software engineers to understand source systems and production data structures.
• Improve the accuracy and availability of business-critical reporting and analytics.
• Establish stronger practices around data quality, documentation, governance, and reliability.
• Identify opportunities to simplify or automate existing data workflows.
• Help establish the data foundation required to support future machine-learning and AI initiatives.

What We're Looking For

Successful candidates will generally bring 3–7 years of experience across analytics engineering, data engineering, data science, or a closely related discipline within a high-growth technology environment.

We're particularly interested in candidates with:

• Advanced SQL skills and experience working with complex production datasets.
• Strong experience designing and maintaining data models.
• Hands-on experience developing production data pipelines.
• Experience working with modern cloud-based data warehouses.
• An understanding of data quality, testing, reliability, and governance.
• Experience working directly with engineering, product, or operations teams.
• Strong analytical and problem-solving skills.
• The ability to take ownership of loosely defined problems and drive them through completion.
• The ability to explain both technical decisions and their impact on the business.
• A track record of improving measurable business or product outcomes through data.

A bachelor's degree in computer science, mathematics, engineering, another quantitative discipline, or equivalent relevant experience is preferred.

Technical Environment

Relevant experience may include:

• Advanced SQL
• Modern cloud data warehouses
• Production data modeling
• Data transformation frameworks
• Python
• Cloud infrastructure
• Infrastructure-as-code tools
• Business intelligence and visualization platforms

Experience with every technology is not required. Strong fundamentals and the ability to work effectively with complex production data are more important than matching a specific toolset.

What Makes a Strong Candidate

We're looking for someone who has done more than maintain dashboards or respond to ad hoc reporting requests.

Strong candidates can clearly explain:

• What data systems or models they personally owned.
• Why those systems needed to be built or improved.
• How they approached complex or unreliable source data.
• How their work affected customers, products, revenue, efficiency, or another measurable business outcome.
• How they collaborated with software engineers and nontechnical stakeholders.
• How the systems they built performed as the organization or data volume grew.

Candidates who have moved between software engineering, data science, and analytics engineering may also bring particularly relevant experience.

Preferred Qualifications

Additional consideration may be given to candidates with experience in:

• High-growth or venture-backed technology companies.
• Data-intensive products or customer environments.
• Regulated industries with complex data requirements.
• Modern data-transformation frameworks and production modeling practices.
• Cloud-based analytics infrastructure.
• Python-based data processing.
• Infrastructure automation.
• Supporting machine-learning or AI-related data requirements.

Working Environment

This is a highly collaborative role that requires regular interaction with technical and business teams.

Candidates should be comfortable working in an environment where priorities can change quickly, data challenges may initially be poorly defined, and individuals are expected to take meaningful ownership of solutions.

Location

The position may be based in Denver, San Francisco, or New York City.

Employees in this role are expected to work from the office four days per week.

Compensation

The anticipated base salary range is $135,000–$180,000, plus equity.

Final compensation will depend on experience, qualifications, location, and overall fit.

Visa sponsorship or transfers may be available for qualified candidates, subject to individual circumstances.

Confidential Search

Additional information regarding the company, industry, technology stack, products, and team structure will be shared with qualified candidates as they progress through the recruiting process.