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 an 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.