Senior Sales Operations Analyst

Morningstar Credit Ratings, LLC
London
2 months ago
Applications closed

Related Jobs

View all jobs

FP&A Manager

Head Of Sales

Senior Data Scientist - Commercial Analytics

Senior Account Manager - Portables

Senior CRM Executive

Change Manager

Senior Sales Operations Analyst page is loaded

Senior Sales Operations Analyst

Apply locations London, Stockholm, Madrid time type Full time posted on Posted 3 Days Ago job requisition id REQ-047275

About Morningstar:Morningstar is a leading global provider of independent investment insights, serving both individual and institutional investors across public and private markets. Our offerings include a wide range of data, research, and investment management services, with $300+ billion in assets under management. Operating in 32 countries, Morningstar supports financial advisors, asset managers, retirement plan providers, and more with comprehensive investment solutions.

The Role:We are seeking a highly skilled Business Intelligence (BI) Analyst with a strong background in finance to join our dynamic team. The ideal candidate will possess deep expertise in financial analysis, reporting, data visualization, and documentation, with proficiency in using Tableau. This role is critical in guiding decisions through data-driven insights across the organization by providing actionable insights, comprehensive financial reporting, and well-documented data sources and definitions.

What You’ll Do

  1. Financial Data Review:
    1. Collect, review, and interpret data to support strategic decision-making.
    2. Perform in-depth financial modeling, forecasting, and variance analysis.
    3. Identify trends, risks, and opportunities within our datasets.
  2. Reporting & Visualization:
    1. Develop, maintain, and optimize Tableau dashboards to visualize complex financial data.
    2. Create and distribute regular financial reports and ad-hoc analyses to stakeholders.
    3. Translate business requirements into technical specifications for Tableau reporting.
  3. Data Management & Documentation:
    1. Ensure data accuracy and integrity by working closely with data engineering teams.
    2. Manage and maintain financial data within BI systems.
    3. Document data sources, definitions, and methodologies for key financial metrics and data points.
    4. Develop and maintain a data dictionary to ensure consistency and clarity across the organization.
    5. Design and implement data quality checks and validation procedures.
  4. Collaboration & Communication:
    1. Collaborate with sales, product, finance, and other departments to understand data needs and provide tailored solutions.
    2. Present insights and recommendations to senior management in a clear and concise manner.
    3. Support our teams during budgeting and forecasting processes by providing data-driven insights.
  5. Process Improvement:
    1. Identify opportunities to enhance BI processes and tools.
    2. Help develop and automate processes for reporting, analysis, and documentation to improve efficiency and accuracy.
    3. Stay updated on industry trends and best practices in BI, finance, and data visualization.

Who You Are

  1. Education:
    1. Bachelor’s degree in Finance, Economics, Business Administration, Data Science, or a related field.
    2. A Master’s degree or relevant certifications (e.g., CFA, CPA, Tableau, Salesforce) is a plus.
  2. Experience:
    1. Experience in business intelligence, financial analysis, or a related field. We’re expecting this role to suit people who have a few years of experience in this field.
    2. Proven experience with Tableau and other BI tools (e.g., Power BI, SQL).
    3. Strong understanding of financial concepts. Knowledge of investment information and databases is a big plus.
    4. Experience in documenting data sources and defining data points.
  3. Technical Skills:
    1. Strong understanding of Salesforce and reporting in Salesforce.
    2. Proficiency in Tableau for data visualization and reporting.
    3. Advanced Excel skills, including pivot tables, VLOOKUP, and financial modeling.
    4. Familiarity with SQL and data querying techniques.
    5. Experience in documenting and maintaining data dictionaries or similar documentation.
  4. Soft Skills:
    1. Excellent communication and presentation skills.
    2. Ability to work collaboratively in a cross-functional team environment.
    3. Self-motivated with a proactive approach to learning and improvement.

Ready to Shape the Future?At Morningstar, every hire we make strengthens our mission to empower investor success. Apply now and help shape the future of investing with us.

Morningstar’s hybrid work environment gives you the opportunity to work remotely and collaborate in-person each week. We’ve found that we’re at our best when we’re purposely together on a regular basis, at least three days each week. A range of other benefits are also available to enhance flexibility as needs change. No matter where you are, you’ll have tools and resources to engage meaningfully with your global colleagues.

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Jobs Careers

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Jobs

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.