Senior Data Analyst

We're hiring a technically strong Senior Data Analyst to help modernize how our FP&A organization uses data, analytics, and automation.

This is an embedded Finance role: you'll report directly to the FP&A Director and work side by side with her day to day while partnering closely with our central Data Analytics and Data Engineering teams on infrastructure, standards, and complex technical problems.

It's a hands-on execution role for someone who builds. Financial and operational data at Welo Global lives across multiple systems - Workday Financials, Adaptive Planning, Salesforce, a Redshift data warehouse, Power BI, and years of Excel workbooks carrying critical processes.

Your job isn't to automate the workbook as-is - it's to understand its purpose, trace its logic to source, figure out what users actually need, and design something better: a rebuilt pipeline, a SQL/Python replacement, a driver-based view, an exception-monitoring workflow, or an AI-assisted tool.

Responsibilities

• Partner directly with FP&A leadership to understand existing reporting, analytical, and planning processes and identify where they should fundamentally change, not just get faster.
• Reverse-engineer complex Excel workbooks and manual processes to understand their data sources, business logic, calculations, and the questions their users are actually trying to answer.
• Design and build automated solutions using SQL, Python, ETL/data transformation, APIs, and modern AI tools.
• Work through large, imperfect, multi-system datasets - resolving data quality, mapping, reconciliation, and grain issues along the way.
• Perform exploratory analysis to identify trends, anomalies, relationships, and underlying business drivers, rather than simply reporting what happened.
• Design dashboards and reporting experiences around the decisions people actually need to make, not a recreation of an existing spreadsheet in a new tool.
• Build AI-assisted, interactive HTML reporting tools - developed with the help of AI coding tools and engineered to refresh efficiently on their own, without repeated AI calls.
• Develop driver-based analysis, exception reporting, and monitoring approaches that surface important changes proactively.
• Explore and build AI-assisted and agentic workflows - including ways for LLMs to interact with data, tools, APIs, and reporting processes.
• Partner with the central Data Analytics and Data Engineering teams on shared data infrastructure and standards - including scoping and coordinating data pipeline and data mart requests.
• Contribute in a leadership capacity within the analytics function even without formal management responsibility - shaping how problems get approached, mentoring by example, and helping set the bar for analytical rigor.
• Translate complex analytical findings into clear, actionable insight for Finance leaders and other business stakeholders.
• Partner in building out our FP&A AI repository - the documentation, conventions, and workflows meant to let an AI assistant execute FP&A processes reliably - and help shape its direction as we go.

Requirements

• 5-10 years of relevant experience required.
• Fluency with data itself - the ability to query, shape, and manipulate data directly.
• An engineering mindset - building automated, repeatable systems (workflows and dashboards that run and refresh on their own, integrations across tools) rather than one-off analysis, and knowing how to scope and coordinate real pipeline or data mart work with Data Engineering.
• An investigative instinct - the ability to look at data and figure out what it's telling us, not just execute a report exactly as specified.
• SQL - comfortable joining tables, tracing grain, finding duplicates, and validating results in unfamiliar schemas.
• Python - practical proficiency for data transformation, analysis, and automation.
• ETL/ELT fluency - strong enough understanding of pipeline design to define data requirements, validate output, and partner effectively with Data Engineering on pipeline and data mart requests.
• Report and dashboard building - able to design and build a reporting/analytical experience end-to-end, whether on a modern BI platform (Power BI, Tableau, Looker, or similar) or as a standalone interactive HTML/web-based report.
• Automation - demonstrated experience automating manual, recurring data or reporting workflows.
• Comfort learning and adopting new AI-assisted tools and workflows - you should be genuinely curious about this space and ready to build fluency quickly on the job.
• Advanced Excel, including the ability to deconstruct sophisticated, undocumented legacy workbooks.
• Demonstrated ability to explore data and figure out why something happened - forming and testing your own hypotheses, not just reporting what happened.
• Fluent English, spoken and written.