Dagster Labs

The harsh reality about breaking into data engineering right now? It's not for the faint of heart. I keep seeing the same questions from hopeful candidates: "Why can't I get a DE job with my boot camp certificate?" or "Why aren't companies responding to my applications?" Here's the unfiltered truth: 1?? Data Engineering is not an entry-level role. A data engineer is a software engineer who understands data-intensive applications and how data needs to be structured for downstream consumption. You need analytical skills to validate your work and soft skills to bridge technical and business worlds. As one DE veteran put it: "I didn't start my career with the title 'Data Engineer'. I'd gamble that a majority of the folks in this field didn't either." 2?? The market for juniors is oversaturated. Between LLMs improving efficiency for senior engineers and economic uncertainty, hiring managers are playing it safe. They're opting for experienced hires with proven track records over developing junior talent. 3?? Geography matters more than you think. Remote work is becoming the exception again. If you're not in SF, NYC, Seattle, or at least a B-tier tech hub, your odds drop dramatically with each mile away. 4?? Personality trumps technical skills. Most successful DEs aren't technical geniuses - they're people others want to work with. No one wants to hire the know-it-all who complains about their current colleagues or thinks leetcode is beneath them. So what now? Consider adjacent roles first - business analyst, data analyst, BI dev. Jobs aren't life sentences. Great careers are built one step at a time. Network with real humans in real places. Solve actual problems instead of collecting certificates. You want a DE job but can't land one? That's not the market being unfair. That's the market telling you something important about your path forward. #DataEngineering #TechCareers #JobSearch #DataScience #CareerAdvice

One of the most painful things about traditional orchestrators is how tightly coupled your workflows are to the environment the orchestrator runs in. Now imagine managing workloads for two different teams—each with strong, valid opinions about which version of a package to use. How do you provision both, without creating chaos? Or say you’re managing pipelines for multiple clients. Do you really need to spin up and maintain multiple orchestrators doing exactly the same thing? Even within a large data team, does centralization mean everyone must share the same codebase and repository? This is where Dagster Labs code locations shine. They allow you to cleanly separate concerns, isolate dependencies, and still manage everything from a central Dagster instance. It’s a game-changer for team collaboration, multi-tenant setups, and platform engineering. I made a short demo to explain what code locations are, how they work, and ways you can start using them to build a more flexible and scalable data platform. Watch it here: https://lnkd.in/dYTvbgdx Demo code link in the comments! #dataengineering #dataplatforms #dagster

Open Source or SaaS for Data Pipelines? An Engineer's Take on the Best Frameworks. Big props to Dagster Labs and their software-defined-assets. https://lnkd.in/g5B57rwW

Tired of testing in prod? We've got just the course for you. Thanks to Dennis Hume, you can learn even more about Dagster with a great course on testing best practices. Learn more advanced skills like using unit tests, mocks, and integration tests with Dagster, all from the comfort of your own home. No better way to build a data platform. No other orchestrator lets you build like a software engineer.

The Dagster community is full of powerful data professionals who are building data platforms that fuel the modern world. Lee Littlejohn is a Lead Machine Learning Engineer at US Foods and has been a Dagster power user for some time. Like he said here, Dagster is at the core of their data platform and enables a large team of analysts and data scientists to perform their jobs with timely and accurate data. Lee will join us for a Deep Dive on April 1st. Register to learn how to replicate his success in your organization. Link in the comments

The Dagster community is full of powerful data professionals who are building data platforms that fuel the modern world. Lee Littlejohn is a Lead Machine Learning Engineer at US Foods and has been a Dagster power user for some time. Like he said here, Dagster is at the core of their data platform and enables a large team of analysts and data scientists to perform their jobs with timely and accurate data. Lee will join us for a Deep Dive on April 1st. Register to learn how to replicate his success in your organization. Link in the comments

Are your teams a victim of the deadliest disease affecting Data Engineering Productivity? That’s right: No-Code Integration Platforms I just read a post from a senior data engineer who shared a painful experience with a popular integration platform. "I lose brain cells every time I work with it," they confessed. "How can anyone build a professional interface without even including 'undo'?" This conversation got me thinking about the true cost of no-code/low-code integration tools in our data stacks. While tools like Workato promise to simplify integrations, they often create more problems than they solve for technical teams. The interface limitations, the frustration when you need anything beyond basic API calls, and the cognitive overhead of learning yet another proprietary system all add up. The most revealing insight? "It MOSTLY removes the need to code, but coding was never the hard part of building things." This hits the nail on the head. The challenging aspects of integration work are: - Understanding the business logic - Ensuring data quality - Handling edge cases - Building proper monitoring - Maintaining the solution long-term For simple "if X happens here, do Y there" scenarios, these tools might work. But in my experience, data integration requirements rarely stay simple. Modern orchestration tools like Dagster offer a more sustainable approach: the flexibility of code with the structure and observability of a purpose-built platform. You get version control, testing, and the ability to leverage the vast ecosystem of open-source libraries. What's your experience? Have integration platforms like Workato delivered on their promises for your team, or have you found more success with code-first approaches? #DataEngineering #DataIntegration #Orchestration #IPaaS #DataPlatforms

SO excited to officially join the Dagster Labs team today!!????

According to a RAND study (link in the comments): AI projects fail at twice the rate of other IT initiatives, with a staggering 80% failure rate. The conventional wisdom points to a shortage of data engineering talent as the primary culprit. But is that really the problem? Here's what I believe is actually happening: There isn't a lack of data engineers – there's a lack of business leaders who understand the value of investing in data engineering. Too many organizations rush to implement AI solutions without building the proper foundation. They want the fruits without planting, nurturing, and waiting for the tree to grow. Most firms can't even get basic descriptive analytics right, yet they expect to successfully implement complex AI systems. This disconnect creates a cycle of failure that's difficult to break. What's particularly concerning is the perception gap. Data engineers are often viewed as "the plumbers of data science" – essential but unglamorous. Yet the reality is that without solid data infrastructure, even the most sophisticated AI models are doomed to fail. The most successful organizations I've worked with recognize that data engineering isn't just a supporting role – it's the backbone of any successful data strategy. They invest in building robust data pipelines, ensuring data quality, and creating systems that can scale. #DataEngineering #AIImplementation #DataStrategy #DataInfrastructure

It's hard to really describe the feeling you get when you write data pipelines with Dagster, so Dennis Hume does a great job of showing you instead. You don't need to associate your data with a single pipeline, but your data exists within the context of all in which you build and what was built before you. Instead of trying to rewrite your pipelines to fit within an Operator, you can just write Python. Many Airflow user are first confused when they can't find a specific integration for their tool in Dagster, but once I tell them that you can just do anything Python does, their eyes light up. You don't need an XtoYOperator and then a YtoZOperator and a ZtoAOperator. You can just write a pipeline that does 3 things as a single asset using Python. Wild! If you love writing Jinja and setting attributes on a task to add documentation, Airflow might be right for you. If you're wondering if there might be a better way, ask your data engineer about Dagster today.