“What exactly do you do all day?”
This is the question I get most often when I tell friends or family that I’m a Data Engineer. Some nod politely, others look at me like I just spoke an entirely new language. And honestly, it’s not surprising—data as a concept is abstract, and the role of someone who wrangles it even more so.
If you’ve ever wondered what a Data Engineer actually does day to day—beyond the vague notion of “moving numbers around”—this blog will give you a peek into the realities of the job, the challenges, the wins, and the small joys that come with working in data.
Job Metadata: Context on My Role
Before diving into my day, it’s helpful to give some context about my setup:
1: I work fully remote, though occasionally I visit our office (about 45 minutes commute by train).
2: My schedule is typically 9 am – 5:30 pm, Monday to Friday. Weekends are thankfully free.
3: I primarily use Python, SQL, and Airflow, while also managing data platforms in cloud environments like Azure and AWS.
4: The data platform I work on is partially mature, which means I often split my time between maintaining existing systems and developing new capabilities.
5: While I work across multiple industries, a large part of my team’s work involves helping our clients as a business analytics services provider, optimizing pipelines, and providing meaningful insights.
In essence, if you’ve heard people say that Data Engineers “move data from one place to another,” that’s not too far from the truth. But it’s much more nuanced: it’s about doing so reliably, efficiently, and in a way that allows analysts, scientists, and business teams to make decisions based on accurate information.
Morning Routine: Business as Usual (BAU)
The first thing I do each morning is check our existing pipelines. Most of the work is batch-processed, meaning data comes in at scheduled intervals, and the job is to ensure it lands correctly in the warehouse.
I check for:
1: Failed jobs in production and pre-production
2: Unexpected delays in processing time
3: Broken connections to source systems
If something is off, I log it and usually propose a fix. Then the team discusses the best way forward. Sometimes it’s a quick fix, sometimes it’s more involved.
After this, I often triage data issues reported by analysts or business users. Maybe a dashboard doesn’t show the expected numbers, or a key report is missing rows. This leads to tracing the data backwards through pipelines—a process I like to call “data detective work.”
Tackling New Development Work
New work usually comes from requests by other teams, whether internal or client-facing. My manager will outline a high-level overview and ask whether it’s feasible and what the timeline might look like. From there, meetings are inevitable—but crucial.
Requirements Gathering
At this stage, the goal is clarity. Stakeholders—sometimes business teams or clients—describe what they want. It’s rare that the first explanation captures the full requirement. Often, what people ask for is more complex than necessary. For example, they might request a sophisticated machine learning model, when the solution could be achieved using simple logic or transformations.
As a Data Engineer working closely with clients and internal teams, I help translate business needs into practical data workflows. A significant portion of my work involves collaborating with business analytics services providers, ensuring the data we deliver is actionable and accurate.
Proof of Concept (POC) → Minimal Viable Product (MVP)
Once requirements are clear, I move into development. I start with a Proof of Concept (POC) to validate that the approach works. Once proven, I iterate to create a Minimal Viable Product (MVP) ready for User Acceptance Testing (UAT).
During this phase, I make key decisions about:
2: Storage solutions and schema design
3: Data visualization solutions integration for dashboards
4: Optimization for speed and cost efficiency
It’s also the phase where I test edge cases and ensure scalability. For example, I may initially test with a sample of data, then scale to the full dataset during UAT. This ensures that the solution works under real-world conditions.
Feedback and Iteration
After UAT, stakeholders review the data and dashboards. Any issues or bugs are iteratively resolved. Once everyone signs off, the code undergoes peer review to ensure best practices, maintainability, and consistency. After approval, the pipeline is deployed to production.
Even after deployment, the work isn’t over. Sometimes users request tweaks, additional metrics, or new features. Balancing these requests with ongoing projects is a key part of the job.
Existing Development Work
Parallel to new development, I maintain ongoing work from previous cycles. This includes:
1: Supporting pipelines already in production
2: Debugging data discrepancies
3: Performing incremental improvements
There are occasional meetings to discuss progress or resolve urgent issues. While less frequent than new development, this maintenance work is critical to ensuring reliability.
Optimizations and Next-Gen Initiatives
A major part of my role is identifying ways to make our platforms faster, cheaper, and more efficient. Recently, I optimized a transformation process that halved compute costs by switching from a low-code tool to a fully scripted approach.
I also spend time refactoring legacy pipelines to improve readability, efficiency, and scalability. Clean, maintainable pipelines reduce future errors and make it easier for new engineers to join the team.
Beyond this, I participate in the design and development of next-generation data platforms. Being involved from the architecture stage is exciting: I help choose the right storage formats, pipeline orchestration, and integration with data visualization solutions that allow end users to interact with the data seamlessly.
Collaboration and Meetings
While it might sound like Data Engineers work in isolation, collaboration is a huge part of the job. I regularly interact with:
1: Analysts and data scientists
2: Product managers
3: Operations teams
4: External stakeholders from business analytics services providers
Meetings are for gathering requirements, discussing progress, or resolving issues. Although sometimes tedious, these interactions ensure alignment and reduce downstream problems.
Tools of the Trade
A typical day involves using a mix of:
1: Python and SQL for coding pipelines and queries
2: Orchestration tools like Airflow for scheduling and monitoring
3: Cloud platforms like AWS, Azure, or GCP
4: Data visualization solutions like Power BI, Tableau, or Looker for dashboards
5: Git for version control
It’s a mix of development, monitoring, troubleshooting, and design thinking.
Tea Breaks, Walks, and Mental Space
Despite the focus on data, small breaks are essential. In the UK, tea breaks are almost sacred—stretching, stepping away from the screen, and enjoying a warm cup of tea helps me stay refreshed. Even a short walk or coffee break can make a huge difference in maintaining focus and creativity.
Challenges of the Role
Being a Data Engineer isn’t without challenges:
1: Abstract Problems – Data is inherently messy, and tracing issues through complex pipelines can be like solving a puzzle.
2: Balancing Priorities – There’s always a mix of urgent bug fixes, long-term improvements, and new requests.
3: Communication – Translating technical challenges into business-relevant language is crucial, especially when working with external business analytics services providers.
4: Scaling Pipelines – Systems must handle growing volumes of data efficiently while staying reliable.
Despite these challenges, the role is rewarding—particularly when pipelines run smoothly, dashboards refresh without error, and stakeholders can make data-driven decisions.
Wins and Rewards
A Data Engineer’s victories often go unnoticed. Unlike a software engineer delivering a visible app, our wins are usually in reliability, efficiency, or enabling insights. Examples include:
1: Reducing compute costs
2: Improving query performance
3:Building pipelines that allow analysts to explore data faster
4: Delivering real-time dashboards via data visualization solutions that empower business users
These small wins accumulate to significant business impact, especially when working with clients who rely on accurate, timely data to make decisions.
Wrapping Up a Day
A typical day ends with reviewing logs, checking for errors, and planning priorities for tomorrow. There’s a sense of accomplishment knowing that the pipelines I build and maintain are powering insights across teams, clients, and sometimes entire organizations.
Although the role involves lots of problem-solving and occasional firefighting, it’s balanced by creativity, autonomy, and the satisfaction of making complex data useful.
Final Thoughts
Data engineering is more than moving data from A to B. It’s about building systems that are reliable, scalable, and meaningful. It’s about bridging the gap between raw information and actionable insights.
Being a Data Engineer often means wearing many hats: developer, troubleshooter, designer, and collaborator. Integrating data visualization solutions ensures that the hard work behind the scenes translates into value for end users. Meanwhile, working closely with a business analytics services provider perspective helps ensure that the data not only exists but drives real business outcomes.
For anyone curious about the role, I hope this gives a realistic view of a typical day. The work is technical, challenging, and strategic, but it’s incredibly rewarding to see the impact of clean, efficient, and well-modeled data.
If you’re interested in more insights from the world of data engineering, or want to know more about specific pipelines, tools, or workflows, feel free to reach out. And if you haven’t already, consider following for more insider perspectives on data, engineering, and analytics.
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