Sr. Engineering Insights Analyst

Sr. Engineering Insights Analyst

Meet the Moment with Alteryx

We're living through a once-in-a-generation shift in how work gets done. Data, automation, and AI are quickly becoming the center of every business decision - and Alteryx is leading the transformation.

You'll be working on the challenges that sit at the heart of modern business. No matter your role, the work you do will help organizations move faster, see more clearly, and tackle questions that used to feel impossible.

If you're ready to meet the moment with innovation, curiosity, and excellence, there's a place for you here.

Alteryx is searching for a Sr. Engineering Insights Analyst. This position is remote-friendly.

Position Overview

We are building a data-driven understanding of how our engineering organization operates, and we’re looking for a Data Analyst focused on Engineering Intelligence to help us do it.

This role sits at the intersection of engineering, data, and operational excellence. Your mission is to transform engineering signals into insights that help us improve software quality, productivity, reliability, and cost efficiency.

You will analyze data from across the software development lifecycle (SDLC), from pull requests and CI/CD pipelines to service reliability metrics and incident management, and translate it into dashboards and insights that guide engineering leaders.

If you are passionate about understanding how engineering organizations work and how they can improve, this role is for you.

What You’ll Work On

Engineering Effectiveness & SDLC Metrics

You Will Help Measure And Improve How Our Engineering Organization Builds And Operates Software. Examples Of Metrics You Will Analyze Include:

• Development & productivity: Pull request volume and cycle time, Code review latency, Deployment frequency, Lead time for changes, Change failure rate
• Reliability & operations: SLO / SLA performance, Incident and escalation patterns
• Engineering quality: Defect trends, Incident root causes
• Platform adoption: Usage of internal platforms and services, Adoption of engineering standards and best practices
• Engineering cost efficiency: Infrastructure cost trends, Cost per service / team, Efficiency of engineering investments

Build Data Products for Engineering

You will create data products used daily by engineering leaders, including:

• Engineering health dashboards
• Service reliability dashboards
• Operational insights for engineering managers
• Executive views of engineering performance
• Adoption dashboards for internal platforms

Dashboards will primarily be built using Superset.

Data Pipelines & Analysis

You will work directly with engineering data systems. Responsibilities include:

• Writing advanced SQL queries to analyze engineering data
• Building lightweight Python pipelines to aggregate and process signals
• Integrating data from sources such as: Git repositories (PRs, commits), CI/CD systems, Incident management systems, Observability platforms, Ticketing systems, Cost management tools

You will help ensure that engineering metrics are accurate, consistent, and trusted across the organization.

Generate Insights That Drive Decisions

Beyond dashboards, your work will focus on finding meaningful patterns in engineering data. Examples include:

• Identifying bottlenecks in the development process
• Detecting reliability risks early
• Understanding operational load across teams
• Measuring whether internal platform investments are working
• Connecting engineering activity with infrastructure costs

You will translate complex datasets into clear insights and recommendations for engineering leadership.

Collaborate Across Engineering

This role requires deep collaboration with engineering teams. You will work closely with:

• Engineering managers
• Platform and infrastructure teams
• DevOps / SRE
• FinOps
• Engineering leadership

You will help define consistent engineering KPIs and ensure teams are producing the right operational data.

Qualifications

Technical Skills

• Strong SQL skills
• Experience using Python for data analysis or pipelines
• Experience building dashboards in Superset, Tableau, Looker, or similar BI tools
• Experience working with complex operational datasets
• Familiarity with data modeling and metric definitions

Engineering Awareness

You should be comfortable working with engineering concepts such as:

• Pull Requests (PRs)
• CI/CD pipelines
• Software Development Lifecycle (SDLC)
• Service Level Objectives (SLOs)
• Incident management
• Escalation tickets
• Service reliability metrics

You don’t need to be a software engineer, but you should enjoy working closely with engineers and understanding how systems are built and operated.

Analytical & Communication Skills

• Strong analytical thinking
• Ability to translate ambiguous questions into measurable metrics
• Ability to communicate insights clearly to technical and leadership audiences
• Strong attention to data quality and metric consistency

What Success Looks Like

Within your first months:

• Engineering leaders have clear dashboards showing service health and engineering productivity
• Reliable metrics exist for SDLC performance and operational health
• Engineering leadership can easily track reliability, quality, and adoption of platforms

Over time:

• Engineering decisions become data-driven
• Operational risks are surfaced early through metrics
• Platform investments can be evaluated through measurable adoption
• The organization develops a shared understanding of engineering effectiveness

Why This Role Is Exciting

Engineering organizations generate enormous amounts of operational data — but very few companies truly understand it. This role will help us answer questions like:

• Are we becoming more productive or slower over time?
• Are our systems getting more reliable?
• Are our platform investments paying off?
• Where are engineers spending their time?
• Are we scaling engineering efficiently and sustainably?

You will help build the operating intelligence layer for engineering.

Compensation

Alteryx is committed to fair, equitable, and transparent compensation. Final compensation is determined by several factors, including but not limited to relevant work experience, education, certifications, skills, and geographic location.

The salary range for this role in the United States is $118,700 - $153,900.

Bonus payouts are based on individual and company performance.

In addition to base pay and bonus eligibility, this role includes clear forms of additional compensation, such as:

• A monthly Connectivity Plus stipend of $150 to support remote work-related expenses
• An annual $200 home office reimbursement

Alteryx offers a comprehensive benefits package designed to support your health, financial security, and overall well-being, including:

• Medical, dental, and vision coverage
• 401(k) with company match
• Paid parental leave, caregiver leave, and flexible time off
• Mental health support and wellness reimbursement
• Career development and education assistance

Interested? Learn more and apply today at alteryx.com/careers!

Find yourself checking a lot of these boxes but doubting whether you should apply? At Alteryx, we support a growth mindset for our associates through all stages of their careers. If you meet some of the requirements and you share our values, we encourage you to apply. As part of our ongoing commitment to a diverse, equitable, and inclusive workplace, we’re invested in building teams with a wide variety of backgrounds, identities, and experiences.

Benefits & Perks

Alteryx has amazing benefits for all Associates which can be viewed here.

For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Alteryx will consider for employment qualified applicants with arrest and conviction records.

This position involves access to software/technology that is subject to U.S. export controls. Any job offer made will be contingent upon the applicant’s capacity to serve in compliance with U.S. export controls.

Key skills/competency

• SQL
• Python
• Data Analysis
• Dashboarding (Superset, Tableau, Looker)
• SDLC Metrics
• Engineering Intelligence
• Reliability Engineering
• Data Modeling
• Communication Skills
• Analytical Thinking