Data Analyst Finance – Your entry into the data-driven world of finance

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As a Data Analyst in Finance you will turn financial, controlling and operational data into clear foundations for decision-making for the CFO, management and specialist departments. You will combine financial acumen with analytical methods and present results in a clear, compelling way.

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The course content at a glance
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• Financial accounting as a foundation, including document management and bookkeeping.
• Accounting basics, including accounts receivable and payable, and sound booking logic.
• Controlling tools for planning, management and KPI tracking.
• Understanding business models and deriving financial implications.
• Data preparation and cleaning for reliable analysis.
• Analytical work using Python and Pandas for structured evaluations.
• Machine learning fundamentals for forecasting and pattern recognition.
• Business intelligence methods for reporting and decision support.
• Interactive dashboards and visualisations.
• Developing reports, KPI systems and visualisations for operational and strategic decisions.
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Start your career path now! 

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What does a Data Analyst in Finance do?

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If you choose to become a Data Analyst in Finance, you will be the interface between finance and data analysis. You will understand balance sheets, P&L, cash flow and planning logic, while also being able to process data from corporate systems to provide clear answers to management questions.

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You will translate topics such as margins, liquidity, risk or growth into structured analyses, reports and forecasts and make relationships in dashboards visible. 

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Below is an overview of your typical tasks as a Data Analyst in Finance:

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• Data collection and preparation: Consolidating, cleaning, structuring, and preparing financial and operational data for analysis, e.g., for P&L, balance sheets, cash flow, or working capital.
  
  
• Creating reports and dashboards: Developing KPI reports, budget vs. actual comparisons, ad-hoc analyses, and interactive dashboards to ensure trends, variances, and drivers are quickly identifiable.
  
  
• Forecasts and scenarios: Forecasting revenue, costs, and liquidity, calculating scenarios, and incorporating results into budget and planning processes.
  
  
• Analysing profitability: Calculating contribution margins, evaluating product, customer, or segment profitability, and identifying potential for improving results.
  
  
• Supporting management: Preparing data-driven decisions regarding pricing, discounts, investments, or resources, and substantiating them with key performance indicators.
  
  
• Improving data and process quality: Standardising KPIs, monitoring data quality, clarifying interfaces, and gradually automating reporting processes.
  
  
• Collaboration and communication: Presenting results clearly, answering queries, and acting as a sparring partner for the CFO, controlling, and specialist departments.

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Find out more now!

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Your career path and skills as a Data Analyst in Finance

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As a Data Analyst in Finance, you will often start in more operational analysis and reporting roles and grows with experience in specialisations or leadership functions . A typical entry point is as a Junior Data Analyst, Reporting Analyst, or Controlling Analyst with a focus on financial data. 

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This is followed by steps towards becoming a Senior Data Analyst Finance or Finance Analytics Specialist, often with responsibility for core KPI logic, forecasts, and planning models. 

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If you want to work in a more strategic capacity, the path opens up into controlling with a data-driven focus, and later into roles such as Analytics Lead, BI Lead, Data Product Owner Finance, or, looking ahead, Head of Finance Analytics.

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To do this, you will of course need some key skills, which you can find below:

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• Advanced Excel skills, including clean data logic and structured reporting.
• SQL expertise to query and consolidate data effectively.
• Experience with business intelligence and dashboards to visualise key performance indicators clearly.
• Fundamentals of Python or R to automate analyses and support forecasting.
• Solid financial understanding of balance sheets, P&L, cash flow, cost accounting, and KPI logic.
• Ability to build, document, and consistently maintain KPI systems and data models.
• Analytical thinking to identify drivers and derive clear recommendations for action.
• Strong communication skills to explain results clearly and present them persuasively.
• Business acumen to align analyses with revenue, margin, risk, and efficiency.
• High level of diligence, as financial insights often form the basis for planning and decision-making.

Tip: If you are still missing some building blocks, you can easily develop them in a structured way through further training at velpTEC .

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Checklist: How to be perfectly prepared for your Data Analyst Finance (m/f/d) job

With this checklist , you can find out whether your profile is already strong enough for Data Analyst Finance (m/f/d) roles and where you can still make targeted improvements.

• Do you have a solid foundation in finance, with a firm grasp of balance sheets, P&L, cash flow, cost accounting, and key KPIs like margins or working capital?
  
  
• Are you skilled at clean data preparation—cleaning, transforming, and modelling data to ensure your analyses are truly reliable?
  
  
• Do you have strong BI skills and the ability to build dashboards that present metrics in a clear, logical, and actionable way?
  
  
• Do you think analytically enough to identify patterns in complex data, pinpoint drivers, and derive concrete recommendations for action?
  
  
• Are you good at data storytelling, explaining results so that the CFO, controlling, and other departments can immediately grasp the core insights and act on them?
  
  
• Are you proficient with tools, such as Excel at a professional level, and can you structure data from typical corporate sources like ERP and CRM systems?
  
  
• Do you have a foundation in forecasting and scenario analysis, allowing you to not just describe trends, but evaluate them with foresight?
  
  
• Do you understand finance processes, such as how reporting, planning, and management are interconnected within a company and which data really matters for them?
  
  
• Are you a confident communicator who can present results, address follow-up questions, and lead clear discussions about figures?
  
  

Learn more about your path to your dream job! 

What can you earn as a Data Analyst Finance (m/f/d)?

Your prospects as a Data Analyst Finance (m/f/d) are future-oriented, because don't forget that almost every company wants to manage its planning, operations, and risks more consistently data-driven . Your profile is exactly what is needed to make decisions faster, more transparent, and more reliable.

The salary for a Data Analyst Finance (m/f/d) position is equally attractive:

• As a Junior Data Analyst, you will typically earn around 35,000 – 45,000 euros gross per year when starting out. 
• With a few years of experience, annual salaries typically range from 55,000 – 70,000 euros.
• Senior Data Analyst roles are compensated at an average of about 68,000 – 89,000 euros gross per year. 

✔️ 2,000+ open positions

✔️ €35,000+ annual gross salary

✔️ A solid and high-demand career field

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Data Analyst Finance (m/f/d) training at velpTEC

The Data Analyst in Finance (m/f/d) training programme at velpTEC follows a learning conceptthat is focused on your goals. You receive a personalized learning paththat is tailored to your prior knowledge and learning style. You learn onlinewith24/7 access, use alocation near you whenever you like, or simply combine both. Furthermore, you can choose to complete your training full-time or part-time.

Throughout the entire process, we support you withexpert adviceand are always here for you. Even after you finish, we are happy to remainyour primary point of contact.

You can also look forward to a high degree of practical relevance and quality standards of the highest level. The training content is structured so that you can apply your knowledge directly in workshops, case studies, and projects. 

Future-oriented topics such as artificial intelligence,big data,blockchain,sustainability and green tech are fully integrated. Classes are taught by highly qualified instructors and supported by a nurturing learning environment. Depending on your personal circumstances, funding is available, potentially covering the full cost.

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FAQ: Data Analyst in Finance (m/f/d)

Can I start without a traditional finance degree?

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Yes. A degree in finance or business administration is not required. Many Data Analysts in Finance come from backgrounds in mathematics, computer science, or data science, or are career changers from controlling, accounting, or business analysis. The key is to understand fundamental financial concepts such as P&L, balance sheets, cash flow, and KPIs. Any missing finance knowledge can be specifically addressed through our practical training programmes.

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Which industries have the highest demand for Data Analysts in Finance?

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This role is in demand across all sectors. Typical fields include banking, insurance, and financial services, as well as manufacturing, retail, e-commerce, energy, logistics, consulting, and larger companies with central controlling or BI departments. This role is relevant wherever planning, forecasting, and performance management are data-driven.

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How much contact will I have with clients or external partners?

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The focus is clearly on internal collaboration. Data Analysts in Finance primarily work with the CFO, controlling, accounting, management, and various business units. External contact is rare and usually project-based, such as during consulting engagements or coordination with auditors.

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How important is English in everyday work?

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English is a significant asset, especially in international companies or corporations. Many technical terms, tools, and documents are in English, as are reports for international stakeholders. While German may dominate in purely domestic companies, solid English skills significantly broaden your career opportunities.

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Is the profession more numbers-driven or communication-focused?

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Both are inseparable. A large part of the work is analytical: preparing data, building models, and calculating key performance indicators. At the same time, communication is crucial because results must be clearly explained, visualised, and placed in a management context. If you love numbers and enjoy explaining them, this is the ideal combination for you.