Effective Communication Skills for Data Engineers

Why Articulation & Stakeholder Skills Matter More Than Ever for Data Engineers As AI gets smarter, writing code isn’t the differentiator anymore. Explaining your work, influencing decisions, and building trust—that’s what sets great engineers apart. That’s why I follow the 50:50 rule: 💡 50% doing the work. 50% talking about the work. My win this month I focused intentionally on building relationships with stakeholders. Alignment takes time, but trust starts small—one clear explanation, one thoughtful question, one consistent follow-through. The result? Stakeholders now see data engineers as partners, not just executors. That shift in perception is the real win. Why this matters for every technical professional You can design the perfect pipeline or architecture… but without articulation, the value gets lost. Strong communication gives you: • Faster decisions • Fewer misunderstandings • Bigger ownership • More strategic influence How to build this skill (practical + fast): 1. Explain your work in business-first language 2. Summarize your updates clearly and proactively 3. Break complexity into simple layers 4. Invest in micro-trust moments with stakeholders AI can generate code. AI cannot build trust. Your technical skills build solutions. Your articulation turns them into impact.

You were just put in charge of the data team at a 2500-person company. And guess what? On day one, the business has already asked about AI and new dashboards. It might be tempting to simply tell your stakeholders "No" or maybe start techno-dumping on why you currently can't implement AI. But that wall of techno babble will simply make their eyes glaze over. You're confusing and not providing clarity. So if you're looking to better to communicate here are a few techniques I use to help get everyone on the same page. 1. Analogies ✅ Do this: Use familiar analogies tailored to their world(do they like to golf, garden, etc) . "AI without reliable data is like building without foundation and on top of sand." ❌ Not that: Don't rattle off system dependencies or mention Kafka, dbt, and data contracts in your first meeting. 2. Impact Framing ✅ Do this: Translate everything into outcomes. "Right now, we can't confidently say which campaigns are actually driving qualified leads, fixing this could help us avoid wasting 100k on a campaign like we did last month." ❌ Not that: "Our data warehouse isn't set up to handle multi-touch attribution at the moment."(ok but why do they care?) 3. Cost of Inaction ✅ Do this: Quantify the downside, "If we skip the groundwork, we risk burning $200K on a model that breaks in production." ❌ Not that: Don't assume vague warnings like "this isn't scalable" will motivate change. 4. Maturity Models ✅ Do this: Show where you are on a crawl-walk-run spectrum, "Right now, we're barely in the 'descriptive' phase; if you ask a question like "How many subscribers did we lose last month due because they had credit cards expire, we wouldn't be able to tell you." ❌ Not that: Don't just say "we're not ready" without context, it sounds like you're saying "We can't" instead of "Here's what comes first." 5. Real-Life Examples ✅ Do this: Share stories of companies that wasted time or money chasing AI too soon. ❌ I guess I don't really know what the opposite is here… Hopefully this was helpful, and let me know if you've used any of these or other techniques to help get on the same page with the business!

One of the skills that earned me 3 promotions in 1 year is… Communication. But not the kind you might think. Most people think communication is just about talking clearly or writing without typos. It’s more than that. It’s about: 1, Understanding your audience: Whether it’s your manager, team, or clients, knowing what they care about changes how you deliver your message. 2, Simplifying complex ideas: Data is full of jargon and numbers. But breaking that down into clear, actionable takeaways? That’s what gets you noticed. 3, Listening to connect, not just respond: Real communication starts with listening. Understanding someone else’s perspective makes your words twice as impactful. So I made sure I could: => Present data-driven insights to technical and non-technical teams without overwhelming them. => Proactively updating my managers on my tasks before they ask. => Help my team with their tasks and let my manager know. I wasn’t just “doing my job” I was someone people trusted to get things done and explain why it mattered. Strong communication isn’t optional. It’s the bridge between doing good work and getting recognized for it. If you master this skill, the opportunities will follow. If you want to get promoted more often, let me know, and I’ll help you communicate your values.

Communicating complex data insights to stakeholders who may not have a technical background is crucial for the success of any data science project. Here are some personal tips that I've learned over the years while working in consulting: 1. Know Your Audience: Understand who your audience is and what they care about. Tailor your presentation to address their specific concerns and interests. Use language and examples that are relevant and easily understandable to them. 2. Simplify the Message: Distill your findings into clear, concise messages. Avoid jargon and technical terms that may confuse your audience. Focus on the key insights and their implications rather than the intricate details of your analysis. 3. Use Visuals Wisely: Leverage charts, graphs, and infographics to convey your data visually. Visuals can help illustrate trends and patterns more effectively than numbers alone. Ensure your visuals are simple, clean, and directly support your key points. 4. Tell a Story: Frame your data within a narrative that guides your audience through the insights. Start with the problem, present your analysis, and conclude with actionable recommendations. Storytelling helps make the data more relatable and memorable. 5. Highlight the Impact: Explain the real-world impact of your findings. How do they affect the business or the problem at hand? Stakeholders are more likely to engage with your presentation if they understand the tangible benefits of your insights. 6. Practice Active Listening: Encourage questions and feedback from your audience. Listen actively and be prepared to explain or reframe your points as needed. This shows respect for their perspective and helps ensure they fully grasp your message. Share your tips or experiences in presenting data science projects in the comments below! Let’s learn from each other. 🌟 #DataScience #PresentationSkills #EffectiveCommunication #TechToNonTech #StakeholderEngagement #DataVisualization

I was having a #mentorship call with a #dataengineer yesterday and we discussed about what makes a data engineer scale well in their career. I was reminded of a medium article I read couple of months ago and it is very fitting here. A typical data engineer is a technical coder. 10x data engineers are better collaborators, thinkers and communicators. Here are the traits that will separate you from the rest: 1. Business Context is King A pipeline that delivers "perfect" data that no one needs is a failure. 10x engineers ask: “How does this data help the company make money or save time?” Understand the "Why" before the "How." 2. The Art of Data Storytelling Stakeholders don't care about your partition logic. They care about insights. Being able to translate complex technical architectures into business outcomes is a superpower. 3. Problem-Solving Over Tool-Picking Don’t be a "Tool Fanatic". A 10x engineer doesn't suggest Kafka because it’s trendy; they suggest it because it’s the right solution for the specific latency requirements of the business. 4. Curiosity & Continuous Learning The data landscape changes every 6 months. If you aren't curious about how new tech (like GenAI or Iceberg) impacts your current stack, you will be obsolete in two years. 5. Extreme Ownership & Accountability When a pipeline breaks at 3 AM, the 10x DE doesn't blame the upstream source. They own the fix, document the "Post-Mortem," and build a circuit breaker to ensure it never happens again. 6. Empathy for the End User Build your tables for the person who has to query them. If a Data Analyst needs a 10-way join to get a simple metric, you’ve failed them. Design for usability. Solid data modeling strategy is key to this. 7. Time Management & Prioritization In data, the "To-Do" list is infinite. 10x engineers know how to say "No" to low-impact requests so they can say "Yes" to the projects that actually move the needle. Ultimately, Mastering the tech stack is the baseline. Mastering the above skills is the multiplier in your career. Original Medium blog written by Jagdesh Jamjala (unable to tag him here for some reason): https://lnkd.in/e_8CPn5B #DataEngineering #CareerGrowth #Mentorship #DataStrategy

You can be the most technical data person in the world. But it doesn’t matter if you aren’t a good communicator. Of course, that takes years of practice. It might be harder to learn than the technical skills. But here's a few hacks you can use to 80:20 your way to effective communication: 1) Put important dashboards on a repeating cadence. The best way to stay both visible and valuable to stakeholders is to give them what they need before they need it. 2) Speak data, not English. A lot of times (it depends on context), stakeholders only want the raw facts—and not the opinions that come with them. 3) The best communicators talk in stories. Storytelling in data is as important a skill as any other. It’s the best way to paint a picture around your findings, and help drive home your conclusions. This might seem contrary to point (2), but a fact-focused story can achieve both. 4) Run training sessions. As a rule of thumb, someone who hasn't been trained on your BI tool won't use it. Aim to run training sessions quarterly, and update users on how to use your stack whenever you make important data assets. 5) Make Loom your best friend. Every data question you get is a key opportunity to educate an end-user (or multiple of them) on your data system. Each time you send an answer, record and share a Loom detailing how the user can find this for themselves. It's something they can go back to, and it's something that might be helpful for multiple people in the future. 6) Share the data on how people are using the data stack. How well you can do this depends on what tools are in your stack. I’ll shamelessly plug our product here—with Zenlytic, Admins can see what their end-users are asking for (and what they wish they could ask for). Sharing success with the team makes them more likely to adopt your tools. 7) Start a plot-of-the-week channel. This is another one that’s great for both visibility and culture-building, not to mention helping familiarize your team with your data function. Share interesting findings, key insights, or anything else you think others within your org will find valuable.

A skill I didn’t think I’d use this much as a data scientist . When I started working with Rensselaer Polytechnic Institute Career Center, I thought Python would carry the weight. I mean, come on, I had cleaned datasets, built models, automated processes. I knew the technical tools. But a few weeks in, I realized something: The real challenge wasn’t just doing the work, it was explaining it. 🗣️Why I structured the data that way. 👉Why I chose one field over another. 👌Why “Other” in a survey shouldn’t just be left alone. Suddenly, it wasn’t about building fancy models — it was about clarity. And that’s when I knew that: Documentation, communication, and clarity are underrated superpowers in data roles. Yes, I cleaned messy files. Yes, I used Power Query and Python Scripts to automate repeat processes. But the thing that made my supervisor say, “This used to take days and now it’s done”? It wasn’t just the automation. It was the fact that the solution made sense to someone else and they could use it without me in the room. That’s value. So here’s to the less talked about skill sets: ✅ Clean documentation ✅ Explaining your choices ✅ Writing clear instructions ✅ Thinking about who will use your work after you If you’re in a data role or just getting started, don’t sleep on the soft skills. They make the technical work matter more. What’s a skill you didn’t expect to use this much in your role? #DataAnalytics #EarlyCareerLessons #CommunicationInTech #SoftSkillsAreHard #LinkedInLearning

The longer I work in Data and AI, the more I realize that communication is the key. It's easy to think that data science, machine learning, or artificial intelligence is all about programming and complex math. While technically true, this is just half the story. 𝐁𝐮𝐭 𝐡𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: Is the value in the technical complexity or in how you communicate it? One phrase I remember from my communication coach now is, "You can always communicate everything." It's a simple phrase, but you can get something from others by communicating it right. How true the word above is reflected in my experience. When working for a client or employed, I was expected to solve problems with my technical expertise. In the early days, I will discuss the solution and result using many technical terms. You know what happens? A mess. Business people mostly do not understand how our technical things work. Many don't even want to know as long as we are solving their problems. Everything is all about the business, after all. When I became a founder, my train of thought also changes: 𝐃𝐨𝐞𝐬 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡 𝐬𝐞𝐥𝐥 𝐢𝐭𝐬𝐞𝐥𝐟 𝐨𝐫 𝐢𝐬 𝐢𝐭 𝐚𝐥𝐥 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐬𝐭𝐨𝐫𝐲? Well, I will answer that it's how you package the tech in a nice story. You can even see it yourself: the most engaging technical content has a story behind it. So, communication is important even if you work in technical fields. Here are some tips you can use to improve communication as data people: ✅𝐊𝐧𝐨𝐰 𝐘𝐨𝐮𝐫 𝐀𝐮𝐝𝐢𝐞𝐧𝐜𝐞: Tailor your message to what matters—code for peers, impact for leaders. ✅𝐓𝐞𝐥𝐥 𝐚 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐲: Structure insights as Problem → Insight → Impact for clarity. ✅𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲 𝐒𝐦𝐚𝐫𝐭𝐥𝐲: Use analogies to relate complex ideas without losing depth. ✅𝐕𝐢𝐬𝐮𝐚𝐥𝐬 𝐖𝐢𝐧: A good chart speaks louder than a thousand data points. ✅𝐄𝐥𝐞𝐯𝐚𝐭𝐨𝐫 𝐏𝐢𝐭𝐜𝐡 𝐑𝐞𝐚𝐝𝐲: Explain your project in 30 seconds—what, why, so what. ✅𝐀𝐬𝐤 𝐟𝐨𝐫 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤: You're on point if non-technical folks get it. ✅𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: Own your insights—clarity with confidence earns trust. Do you have any experience and tips you want to share? Discuss it below!👇 Want to learn more and get daily data science tips in your email inbox? Subscribe to my Newsletter>>> https://lnkd.in/g639tmpD ——————— You don't want to miss #python data tips + #datascience and #machinelearning knowledge + #AI. Follow Cornellius Y. and press the bell 🔔 to learn together. ———————

Job descriptions say “strong communication skills.” They rarely explain what that looks like day to day. In analytics, it usually comes down to 𝗳𝗶𝘃𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀 you have over and over again. Here’s what they are: → 𝗧𝗵𝗲 𝗶𝗻𝘁𝗮𝗸𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 “Can you pull some numbers?” becomes “What decision are you trying to make, and what would ‘good’ look like here?” → 𝗧𝗵𝗲 𝘀𝗰𝗼𝗽𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 “Here’s what this analysis 𝘸𝘪𝘭𝘭 answer. Here’s what it’s not designed to do. Here are the trade-offs we’re making given the data and time we have.” → 𝗧𝗵𝗲 𝗺𝗶𝗱-𝘄𝗮𝘆 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 “Here’s what we’re seeing so far. Does this line up with what you’re noticing on the ground?” → 𝗧𝗵𝗲 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 “Here’s what I recommend, why I’m confident in it, and what it means for risk, effort, and impact.” → 𝗧𝗵𝗲 𝗳𝗼𝗹𝗹𝗼𝘄-𝘂𝗽 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 “Here’s what we decided, what happened, and what we want to learn for next time.” You don’t need perfect scripts. You need the courage to start these conversations instead of waiting to be asked. That’s how you shift from “report builder” to trusted partner. Which one of these five feels most uncomfortable for you right now? PS: Follow Seth for insights on how you can level up your communication skills as a data professional