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Job Description Template

Data Analyst (Mid-Level, SMB) Job Description Template

A Data Analyst (mid-level, 3-5 years experience) in a small-to-mid-sized business is responsible for turning raw data into actionable insights that inform business decisions . They collect, clean, and analyze data from various sources, then translate the numbers into clear findings and recommendations for stakeholders . In an SMB environment, the mid-level analyst often handles end-to-end data tasks - from gathering data and managing reports to presenting results - working both independently and collaboratively. The role bridges the gap between data and decision-making by not only performing technical analysis but also communicating the "story" behind the data in plain language for non-technical teams . This position is typically hybrid (remote-friendly with some on-site days), so the analyst must be effective using online collaboration tools and also comfortable with face-to-face meetings when needed. Ultimately, a mid-level Data Analyst helps the company make evidence-based decisions by providing accurate analyses, dashboards, and insights that drive strategy and improve operations.

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Data Analyst (Mid-Level, SMB) Responsibilities

  • Data Collection & Cleaning: Gather data from multiple internal and external sources (databases, spreadsheets, APIs, etc.) and clean or validate it to ensure accuracy and consistency before analysis
  • . This includes handling missing values, removing duplicates, and resolving any inconsistencies in datasets.

  • Data Analysis & Pattern Identification: Explore and analyze large datasets to identify trends, correlations, and anomalies that align with business questions
  • . Use statistical methods and critical thinking to interpret what the data is revealing about business performance or customer behavior.

  • Database Querying (SQL): Write and execute SQL queries or scripts to extract relevant data for analysis from relational databases
  • . Optimize queries for efficiency and accuracy, ensuring the right data is pulled to answer specific business inquiries.

  • Reporting & Visualization: Develop reports and interactive dashboards to present data in a meaningful way, using tools like Tableau or Power BI (or equivalents)
  • . Summarize complex data into charts, graphs, and tables that highlight key metrics and insights for decision-makers.

  • Insights Presentation: Present and communicate key findings to stakeholders and leadership, translating technical results into clear business insights
  • . This involves preparing summaries or slide presentations and emphasizing the implications of the data in terms that various business teams (e.g. marketing, finance, operations) can understand and act on.

  • Cross-Functional Collaboration: Work with cross-functional teams - for example, collaborating with business managers to clarify their data needs and with IT or data engineers to improve data pipelines and sources
  • . Help define and refine key performance indicators (KPIs) and ensure everyone uses consistent metrics

    .

  • Continuous Improvement of Data Systems: Maintain and enhance the data analysis infrastructure, such as updating reporting systems or data models for new business requirements
  • . Stay

    updated on industry trends and new analytics tools, suggesting improvements to current processes to increase efficiency or data quality over time.

    Required Skills & Qualifications

    Preferred Soft Skills

  • Communication: Excellent written and verbal communication skills are a must
  • . The analyst needs to explain complex data findings in simple terms, whether writing a summary email or presenting to a group. They should adjust their language to the audience, ensuring that even non"technical stakeholders grasp the insight and significance of the data.

  • Problem-Solving & Critical Thinking: A strong problem-solving mindset to approach data questions methodically
  • . The analyst should be able to break down vague business problems into analytical steps, interrogate the data critically, and validate whether findings truly explain the issue. Critical thinking also involves being skeptical of initial results and double-checking for biases or errors.

  • Attention to Detail: High level of attention to detail to avoid mistakes in analysis and reporting This means carefully checking calculations, ensuring data integrity, and catching inconsistencies.
  • Small errors can lead to faulty decisions, so a successful analyst double-checks their work (and has quality control steps) before delivering results.

  • Collaboration & Teamwork: Ability to work collaboratively with others, including technical teams and non-technical colleagues
  • . In practice, this means being open to feedback, sharing knowledge with teammates, and being able to gather requirements or clarify needs through active listening. A mid-level analyst in an SMB might often serve as the liaison between data and various departments, so being approachable and cooperative is key.

  • Time Management & Organization: Strong organizational skills to handle multiple projects or data requests and meet deadlines
  • . The analyst should be capable of prioritizing tasks based on business urgency and managing their time in a semi-autonomous hybrid work setting. This includes keeping track of regular reporting schedules while also tackling ad-hoc analysis requests.

  • Adaptability: Flexibility and adaptability in a fast-changing environment. SMBs often evolve quickly, and data needs can shift as the business grows or priorities change. The analyst should be comfortable adjusting their focus, learning new tools or techniques as needed, and handling some ambiguity. (For example, adapting from one software to another if the company adopts new technology.)
  • Presentation Skills: (Related to communication) - ability to present data insights confidently in meetings or via video calls. This includes using storytelling techniques to make the data memorable and using visuals effectively. While not every analyst is a formal presenter, mid-level roles often involve briefing managers or teams on what the numbers mean.
  • Interview Questions for Data Analyst (Mid-Level, SMB)

    1. correctness (like writing a syntactically correct SQL query or accurately describing a method) is scored, as well as the quality of approach (do they hit all the important steps?
    2. If the candidate cannot clearly articulate their thoughts or struggles to explain technical concepts in simple terms, that"s a warning sign . A data analyst who "knows their stuff" but can"t communicate it will have a hard time driving any action from their insights. For example, rambling, using excessive jargon, or failing to answer the question directly in the interview or written tasks could in
    3. If the candidate doesn"t catch an inconsistency that was intentionally placed in the test, it calls into question the quality of work they"d produce on the job.
    4. If the candidate seems "nose-deep in data with blinders on" and fails to understand or care about the business context
    5. If a candidate misses one of the segments, they lose those points. For example, if they outline steps but never mention checking the data correctness, deduct the validation points. A very strong candidate might also mention time management or setting milestones, which isn"t explicitly required but would show thoroughness. We"d still cap at 10 points (with possibly a bonus point in mind to differen
    6. Describe a time you had to explain a complex data insight or report to someone who isn"t familiar with data (for example, a senior manager or a client). How did you approach it, and what was the result?
    7. Give an example of a challenging interaction with a stakeholder or team member in the context of a data project. Perhaps a situation where they disagreed with your analysis or wanted something unrealistic. How did you handle it?

    Frequently Asked Questions

    What does a Data Analyst (Mid-Level, SMB) do?

    A Data Analyst (mid-level, 3-5 years experience) in a small-to-mid-sized business is responsible for turning raw data into actionable insights that inform business decisions . They collect, clean, and analyze data from various sources, then translate the numbers into clear findings and recommendations for stakeholders . In an SMB environment, the mid-level analyst often handles end-to-end data tasks - from gathering data and managing reports to presenting results - working both independently and collaboratively. The role bridges the gap between data and decision-making by not only performing technical analysis but also communicating the "story" behind the data in plain language for non-technical teams . This position is typically hybrid (remote-friendly with some on-site days), so the analyst must be effective using online collaboration tools and also comfortable with face-to-face meetings when needed. Ultimately, a mid-level Data Analyst helps the company make evidence-based decisions by providing accurate analyses, dashboards, and insights that drive strategy and improve operations.

    What qualifications does a Data Analyst (Mid-Level, SMB) need?

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