How to hire data talent in 15 days without lowering standards

Absolutely agree with this, Tommaso — the best data professionals bring a blend of technical fluency, storytelling, and stakeholder engagement. But I’d add that commerciality is the glue that binds it all together. You can build the most elegant model or dashboard, but if it doesn’t drive a decision, shift a KPI, or unlock value, it will fail. The ability to connect data work to business outcomes — to ask “so what?” and “what now?” — is what sets great analysts apart. That’s where the real impact lies.

Still chasing the mythical data unicorn? 🦄 The one who codes, presents, and somehow translates it all into strategy? Well… the triangle just shifted. 💼 𝐂𝐨𝐦𝐦𝐞𝐫𝐜𝐢𝐚𝐥𝐢𝐭𝐲 has taken centre stage and is now running the show. In this market, no one cares how beautiful your dashboards are if they don’t move a number that matters! 💸 Revenue 💎 Retention 💰ROI I’ve seen it again and again in recent briefs and conversations I have with hiring managers ➡ they’re hiring growth enablers. People who can explain data impact in business language. And that’s where many good analysts are still falling short. Data is no longer about reporting what happened. (Which is what most analysts are doing) It’s about helping decide what happens next. If you’re hiring in data right now, stop making technical tests your north star. Ask the bigger question: “How does this person make the business money?” Here's where the true data unicorns are hiding 🦄 Who of the data leaders in my network found a smart way to assess commerciality during their hiring process? #DataAnalytics #Hiring #DataJobs

It’s funny how roles evolve in data teams. You’re hired as a Data Analyst but then— “Can you do a bit of analysis?” → Sure. “Can you make a report?” → Yes. “Dashboard?” → Yes. “Machine learning?” → Uh, yes. “Data pipeline?” → …also yes. The lines between Analyst, Engineer, and Scientist are blurrier than ever. Titles change, but curiosity and adaptability remains the same. #data #analytics #datascience

Ever felt like a "data unicorn" in your job? I recently came across a job posting that wanted one person to handle all data roles. It got me thinking about the distinct skills each role requires. For me, each data role is like a piece of a puzzle. Data Analysts interpret the past, Data Engineers build the infrastructure, Data Scientists predict the future, and Analytics Engineers ensure everything runs smoothly. Combining these roles into one position can lead to burnout. Here's my take: 1. Understand your strengths and focus on them. 2. Communicate your role clearly to avoid unrealistic expectations. 3. Advocate for specialized roles in your organization. Have you ever been in a "do-it-all" data role? How did you manage it? #DataScience #CareerAdvice

Data without people is just noise. We talk a lot about data strategy, but not enough about people strategy. Without skilled data engineers, scientists, and architects, insights stay buried in systems. The technology exists - but it’s talent that turns it into impact. You can automate workflows. You can’t automate creativity. #DataScience #Analytics #Leadership #Hiring

Last week a friend asked me, “Are you a Data Analyst or a Data Scientist?” I smiled and said, “Depends on the business question of the day.” Monday: Writing SQL to rescue data from creative storage solutions. Tuesday: Stress-testing models that predict everything except stakeholder priorities. Wednesday: Engineering data pipelines that elegantly break at scale. Thursday: Crafting dashboards that make complexity look effortless. Friday: Translating technical noise into executive narratives — occasionally with empathy. By Friday evening, I’ve been an analyst, a scientist, an engineer, and a part-time diplomat. In modern organizations, roles are fluid — what matters isn’t the title on the slide, but the value in the insight. #DataLeadership #Analytics #DataStrategy #DigitalTransformation #CorporateHumour

I came across a job posting today that said, “Need a data person to build pipelines, analyse reports, run ML models, and create dashboards.” It sounded like a classic one-size-fits-all job description. However, it actually encompasses four distinct roles: 1. Data Analyst: They take existing data and interpret its meaning, answering questions like, “What happened?” 2. Data Engineer: Their role is to build and maintain data pipelines and warehouses, essentially creating the data infrastructure. 3. Data Scientists: They conduct experiments, develop models, and make predictions, asking questions like, “What might happen if we…?” 4. Analytics Engineer: Their focus is on cleaning, structuring, and modelling data to facilitate easy analysis, ensuring analysts aren’t spending half their time fixing spreadsheets. In summary: - Engineers build the infrastructure. - Analysts drive on it. - Scientists predict the traffic. - Analytics Engineers ensure the GPS actually works. These roles require different skills and expertise, yet companies often lump them together, leading to burnout after just three months. We need to stop treating “data” as a one-size-fits-all concept. #dataengineer #azuredataengineer #datascience #datascientest #dataflow #ETL

When your title says one thing, but your day-to-day says another. On paper, you can have one data role (i.e. Data Analyst). In practice, you can have another (i.e. BI Engineer). And that’s the thing — the more I talk to people in data, the more I realize how blurry the lines between roles have become. A Data Analyst builds insights and visualizations. A BI Engineer focuses on modeling (dbt, LookML), and scalable reporting infrastructure. An Analytics Engineer bridges the gap — applying software engineering principles to analytics work. But in reality? Many of us wear a mix of these hats every single day. Titles often depend on the company’s structure, not necessarily the scope of our skills. I’m curious — how do you see these roles in your organization? Do you also find yourself blending multiple data roles in practice? 👇 Let’s discuss — I’d love to hear how others experience this overlap. #data #analyticsengineering #biroles #datateam #careerdevelopment

Industry Insight/Trend Did you know most companies lose key insights because their data isn’t cleaned before analysis? This week, I learned how data cleaning improves business decisions—here’s why it matters for your next hire! #DataCleaning #BusinessGrowth #DataScience #Hiring #AnalyticsJobs #LinkedInIndia #JobOpening