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Financial Data Analyst Jobs (NOW HIRING)

MDAEdge

Power BI Financial Data Analyst

Louisville, KY · On-site

MDAEdge is seeking a business-savvy Power BI Financial Data Analyst with expertise in Power BI and SQL. The role involves building executive-ready dashboards, designing SQL-driven data pipelines, and ...

Xcel Energy

Sr Financial Data Analyst

Denver, CO · On-site

$73K - $104K/yr

Analyze financial data to identify adverse trends and recommend solutions to mitigate financial impact. Effectively communicate results through concise oral and written communications. * Provide ...

Xcel Energy

Sr Financial Data Analyst

Minneapolis, MN · On-site

$73K - $104K/yr

Analyze financial data to identify adverse trends and recommend solutions to mitigate financial impact. Effectively communicate results through concise oral and written communications. * Provide ...

Xcel Energy

Sr Financial Data Analyst

Minneapolis, MN · On-site

$73K - $104K/yr

Analyze financial data to identify adverse trends and recommend solutions to mitigate financial impact. Effectively communicate results through concise oral and written communications. * Provide ...

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Financial Data Analyst information

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How much do financial data analyst jobs pay per year?

As of Aug 6, 2026, the average yearly pay for financial data analyst in the United States is $85,459.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,500.00 and $100,500.00 per year, depending on experience, location, and employer.

What is a financial data analyst?

Financial Data Analysts are professionals who collect, process, and analyze financial data to help organizations make informed business decisions. They use statistical tools and financial modeling techniques to interpret data, identify trends, and forecast future financial performance. Their work supports budgeting, investment strategies, risk assessment, and overall financial planning. Financial Data Analysts often collaborate with other departments to provide insights that drive business growth and efficiency.

What does a financial data analyst do?

Financial data analysts examine financial records and prepare comprehensive reports for their company. These reports are used to identify the current direction of production and help managers forecast future trends. As a financial data analyst, your job duties include compiling data from the market and various departments within the company, analyze the information, and create reports for upper management. The qualifications to pursue a career as a financial data analyst include a bachelor’s degree in finance, economics, accounting, or a related field and industry experience.

Is it difficult to become a financial data analyst?

Becoming a financial data analyst typically requires a strong foundation in finance, accounting, or related fields, along with skills in data analysis tools like Excel, SQL, or Python. Gaining relevant experience and certifications such as CFA or CFA Level I can also enhance job prospects, but the difficulty varies based on individual background and effort.

What are the key skills and qualifications needed to thrive as a financial data analyst, and why are they important?

To thrive as a Financial Data Analyst, you need strong analytical skills, a solid understanding of finance and accounting principles, and a bachelor's degree in finance, economics, or a related field. Proficiency in data analysis tools such as Excel, SQL, Python, and financial modeling software, as well as certifications like CFA or FRM, are often expected. Attention to detail, problem-solving abilities, and clear communication skills set exceptional analysts apart in interpreting and presenting complex data. These skills are vital for delivering accurate financial insights that inform business decisions and drive organizational success.

What are some common challenges financial data analysts face when communicating their findings to non-technical stakeholders?

Financial Data Analysts often encounter the challenge of translating complex quantitative data and statistical analyses into clear, actionable insights for non-technical stakeholders. This requires not only a deep understanding of financial concepts but also strong communication skills to present data visually and narratively. Bridging this gap can involve tailoring presentations, using data visualization tools, and focusing on the business implications of the analysis rather than technical details. Effective collaboration with cross-functional teams, such as finance, operations, and management, is key to ensuring that data-driven recommendations are understood and implemented.
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Financial Data Analyst jobs near you
Infographic showing various Financial Data Analyst job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $85,459 per year, or $41.1 per hour.

Data Analyst, Financial Data Engineering

Stripe

New York, NY • On-site

Full-time

Posted 14 days ago

Job description

Who we are
About Stripe
Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world's largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.
About the team
Data Science at Stripe is a vibrant community where data analysts and data scientists learn and grow together. You'll work with some of the most fundamental data at Stripe, and use that data to help drive company-wide initiatives. We have a variety of Data Analytics roles and teams across Stripe and Data Analysts are hired in line with the business needs and domain of the organization they will support.
What you'll do
In this role, you'll partner deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. You'll design, build, and own the scalable data infrastructure that powers analytics and reporting across the company.
Day to day, you'll translate complex business requirements into reliable data models, own end-to-end pipeline development from raw data ingestion to clean, consumption-ready datasets, and work with leaders to prioritize the highest-impact data investments. You'll go beyond building dashboards-you'll architect the data layer that makes self-service analytics possible and deliver actionable business recommendations through rigorous analysis and data storytelling.
Responsibilities
  • Design, build, and maintain scalable data pipelines and ETL/ELT workflows that power production-grade financial reporting, risk measurement, and operational decisioning for Treasury Finance
  • Leverage AI tools (code assistants, LLM-based agents) to accelerate pipeline development, data quality automation, reconciliation, and documentation - expanding technical scope while maintaining quality.
  • Model and transform raw data into clean, well-documented datasets that serve as the core foundations for decision making for Treasury Finance (e.g. float positions, cash explainability, risk exposures, liquidity management)
  • Establish and enforce data quality standards through testing, monitoring, and alerting on pipeline health
  • Establish and own data freshness SLAs, operational alerting, and incident response for your data domains - ensuring production reliability for risk and finance critical workflows
  • Partner deeply with Treasury Finance, data scientists/analysts, and engineers to define data requirements and deliver trusted, reusable financial data products
  • Partner deeply with Treasury Finance stakeholders to translate business requirements into data architecture decisions, anticipating needs and helping to drive data strategy rather than reacting to requests
  • Build self-service tooling and analytics layer that empower stakeholders to access and explore trusted data autonomously
Who you are
We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
Minimum requirements
  • 6+ years of full-time experience in Data Engineering, Analytics Engineering, Business Intelligence Engineering, or a related analytical role
  • Proficiency in SQL, including complex query optimization and data modeling
  • Proficiency in Python for data pipeline development, not just scripting
  • Experience with distributed data frameworks like Spark to write and debug data pipelines
  • Experience with workflow orchestration tools (e.g. Airflow, Flyte, or equivalent)
  • Proven ability to design, implement, and maintain production-grade data pipelines and dashboards
  • Good understanding of development processes and best practices like engineering standards, code reviews, and testing
  • Ability to clearly communicate results and drive impact with cross-functional partners
  • Experience owning production data products with defined quality standards, testing, and documentation
Preferred qualifications
  • Prior experience at a growth-stage internet or software company
  • Prior experience working with Finance or Treasury teams
  • Understanding of treasury and finance concepts (e.g., float positions, FX exposure, cash reconciliation, balance sheet usage, liquidity management)
  • Experience with data quality frameworks, data contracts, tiering/classification, or SLA management
  • Experience creating leadership-level reporting, such as QBRs and MBRs
  • Experience building financial reporting infrastructure - e.g. automated treasury processes, regulatory reporting, or finance close
  • Proficiency with AI tools (code assistants, LLM agents) to accelerate pipeline development and data quality automation
  • Interest in how data products enable automated/agentic workflows - understanding that data quality determines the reliability of every downstream decision

Apply