Manager, Finance Analytics Engineering & Automation

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 Manager, Finance Analytics Engineering & Automation . This position is remote-friendly.

Position Overview:

Alteryx is looking for a hands-on manager who sits at the intersection of Finance, analytics engineering, and automation. This role will turn Finance requirements into trusted analytical data products, reusable consumption layers, automated workflows, controls, and integrations. You will work closely with FP&A, Finance Data & Analytics, Data Engineering, and business partners to make governed enterprise data easier for Finance to use while improving reliability, scalability, and decision support.

This is not a central data engineering role and it is not a traditional Finance systems administration role. Enterprise Data Engineering will continue to own deeper platform and production-pipeline engineering. This role sits closer to Finance: understanding the business question, tracing source data and lineage, shaping Finance-ready analytical layers, and building repeatable solutions that are controlled, supportable, and useful for decision-making.

Primary Responsibilities:

Own Finance analytics engineering and automation across Snowflake, SQL, workflow automation, controls, integrations, and downstream Finance consumption.

Translate Finance requirements and approved business definitions into scalable technical solutions, working with Finance Data & Analytics on product priorities and with Data Engineering on enterprise pipelines and platform standards.

Build and maintain Finance-ready consumption views, analytical models, reconciliations, and reusable datasets that make governed enterprise data easier for FP&A and Finance users to consume without unnecessary duplication.

Perform hands-on SQL analysis, lineage tracing, impact assessment, and root-cause investigation across Finance data flows, especially when definitions, sources, joins, or upstream systems change.

Establish and automate technical controls for critical Finance processes, including reconciliations, data-quality checks, regression tests, monitoring, release validation, and recovery procedures.

Lead Finance automation and workflow engineering, setting standards for maintainability, production support, documentation, ownership, and reusable patterns across tools such as Alteryx, Python, APIs, or comparable platforms.

Serve as a primary Finance technical partner to Data Engineering, helping separate Finance-owned analytical logic from deeper platform engineering while ensuring clean handoffs and clear ownership.

Support the technical architecture and productionization of AI-assisted Finance workflows and analytical agents, with emphasis on governed sources, deterministic execution where needed, traceability, testing, and safe failure.

Manage and develop Finance automation engineering capacity, including coaching team members, strengthening backup coverage, and building durable technical capability rather than key-person dependency.

Partner across FP&A, Accounting, RevOps, Product, IT, and other teams to deliver cross-system solutions that are practical for Finance users and sustainable in production.

Qualifications:

• 6+ years of experience in FP&A, finance analytics, analytics engineering, financial data, automation, or a related technical Finance role.
• Strong Finance or FP&A domain knowledge and the ability to understand how business definitions, planning processes, reporting, and controls translate into technical requirements.
• Strong hands-on SQL skills and experience working in Snowflake or a comparable cloud data platform, including data modeling, lineage analysis, validation, troubleshooting, and building reporting- or consumption-ready datasets.
• Demonstrated ability to move from an ambiguous Finance question to a trustworthy data answer by interrogating source data, challenging assumptions, and validating results.
• Experience building or supporting analytical consumption layers, semantic or reporting-ready datasets, reusable views, or similar Finance-facing data products.
• Experience building automated analytical workflows using tools such as Alteryx, Python, dbt, APIs, or comparable platforms. Deep Alteryx expertise is not required.
• Working knowledge of production controls, testing, monitoring, documentation, change management, and root-cause analysis.
• Prior people management or strong technical-lead experience, with the ability to coach hands-on practitioners while also setting technical direction.
• Valued Skills: Experience in a SaaS or subscription business, including exposure to ARR, bookings, renewals, revenue, or other recurring Finance metrics.
• Experience partnering with centralized Data Engineering teams while owning Finance-specific analytics, consumption, or automation layers.
• Familiarity with ERP, CRM, planning, HRIS, and reporting platforms such as NetSuite, Salesforce, Anaplan, Workday, or Tableau.
• Experience with data governance, semantic layers, metadata, lineage, or certification practices.
• Experience evaluating or productionizing AI-assisted analytics, LLM, agent, or MCP-style workflows where accuracy, traceability, and controls matter.

What Success Looks Like:

Finance can consume governed enterprise data through clear, reusable analytical layers without creating unnecessary duplicate assets.

Complex data questions and production issues can be diagnosed and resolved without depending on a single senior leader to perform the technical investigation.

Finance automation and analytical workflows have clear ownership, controls, documentation, monitoring, and backup coverage.

Finance and Data Engineering operate with clear boundaries and strong partnership: Finance owns business-facing analytical logic and consumption, while Data Engineering owns deeper enterprise pipeline and platform engineering.