Senior Data Analyst

About Welo Global

Welo Global is a leader in multilingual AI, technology, and content solutions serving over 2,000 clients in 300 languages. The company combines globally scaled multilingual infrastructure, including a network of over 500,000 linguists and domain experts, with advanced NLP, computational linguistics, and best-in-class compliance backed by seven ISO certifications. Welo Global’s five brands—Welocalize (multilingual content and localization services for global enterprises), Park IP (intellectual property and patent translation services for law firms and corporate legal teams), Welo Life Sciences (regulated language and compliance-aligned content solutions for pharmaceutical, biotech, and medical device organizations), Adapt (multilingual performance-led digital marketing agency), and Welo Data (multilingual data generation, evaluation, and human data infrastructure for AI systems)—serve distinct customer segments with purpose-built expertise, fit-for-purpose solutions, and supporting technology.

About the Role

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 Welocalize lives across multiple systems - Workday Financials, Adaptive Planning, Salesforce, a Redshift data warehouse, Power BI, and years of Excel workbooks carrying critical processes. Much of it doesn't connect cleanly, and a lot of institutional knowledge lives in undocumented spreadsheets, manual processes, and Power Queries rather than any system of record.

You might start with a complex, years-old Excel workbook and a simple prompt: “This is how we do this today.” 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.

We're just starting to build out an FP&A AI repository - documentation, business logic, and workflows meant to let AI reliably execute FP&A processes, not just answer one-off questions - and you'll partner with us to determine its direction.

This role is a deliberate combination of three things:

1. Fluency with data itself - the ability to query, shape, and manipulate data directly.

2. 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.

3. 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.

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.

Technical skills matter, but how you approach problems matters just as much. We're looking for someone who:

• Is genuinely curious and naturally asks “why?” when something doesn't reconcile or doesn't make sense.

• Enjoys digging into messy or unfamiliar data and figuring out how things connect.

• Doesn't assume an existing process or report is right simply because that's how it's always been done.

• Can take an ambiguous problem and independently structure an approach to solving it, while knowing when to pull others in rather than spinning wheels alone.

• Looks beyond the initial request to identify opportunities, risks, and better approaches the requester may not have thought to ask for.

• Enjoys collaborative problem-solving - bouncing ideas around, testing hypotheses, and challenging assumptions to get to a stronger answer.

• Takes ownership of the areas they work in - the business purpose, the data, the known issues, and the opportunities for improvement - rather than treating each request as a one-off ticket.

• Likes building things, experimenting, and continuously improving how work gets done.

5-10 years of relevant experience required. You should be genuinely, hands-on strong across:

• 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. Hands-on pipeline-building experience is a plus.

• 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. Standalone HTML/web-based reports are increasingly our primary format.

• 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. This is a fully remote role and you'll communicate directly, in English, with business stakeholders and executive leadership as well as data analysts and engineers across our global organization, where English is the shared working language.

Bachelor's degree in Computer Science, Information Systems, Data Science, or a related quantitative/technical field strongly preferred; equivalent experience acceptable. Master's degree is a plus.

• Experience supporting Finance, FP&A, Strategy, Operations, or another analytically intensive business function.

• Familiarity with financial concepts such as revenue, margin, profitability, forecasting, and variance analysis.

• Experience modernizing legacy Excel-based reporting or analytical processes.

• Experience working in an environment where data is distributed across multiple systems and isn't always perfectly documented.

• Experience working effectively in a fully remote, distributed team environment.

• Exposure to AI/agentic tooling, or experience helping design how AI gets applied to a business process (e.g., LLM workflows, tool/function calling, RAG, MCP, building AI-assisted reporting or automation).

• Machine learning, statistical modeling, or formal data science experience is welcome but not required.