From Psychology Plans to Data Impact: Michelle Hackl, Lead Data Engineer at WienIT, on Responsibility, Azure/Power BI, and Growing into a Tech Lead

Context: Title, Speaker, Company

• Title: Michelle Hackl, Lead Data Engineer bei WienIT
• Speaker: Michelle Hackl
• Company: WienIT

At DevJobs.at we listened to Michelle Hackl outline, with candid clarity, how a late spark for programming can evolve into a Lead Data Engineer role, and what it truly takes to run data projects that matter. Her headline principle is disarmingly simple: make data usable. Her stack is Azure and Power BI. Her day-to-day is defined by responsibility, cross-team orchestration, and delivering reports that turn raw inputs into decisions.

The turning point before graduation: discovering programming

Michelle Hackl initially planned to study psychology. Shortly before graduating from school, a friend showed her the command line, and that moment reshaped her path. Computers had always interested her, but the first hands-on programming experience transformed curiosity into direction.

She began programming at university and quickly realized how much fun it was. She describes having real fun with it, and that joy became the engine for a decisive change: she switched and studied Data Science in a four-year program in the United States. From there, she moved straight into the professional world, satisfied with the choice she had made.

For us, the lesson is straightforward: the timing of your first coding steps matters less than the energy you bring to them. What counts is sustained engagement and the satisfaction that comes from solving problems.

What a Lead Data Engineer does at WienIT

Hackl introduces her role on two levels: Tech Lead and Lead Data Engineer. In practice, she is responsible for the Azure and Power BI stack. Her team pulls data, processes it, stores it, and then presents it in reports. That spans cloud data storage, data warehouse structures, and reporting — the full journey from source to executive insight.

This journey has a purpose beyond tools and pipelines: making data usable. Hackl underscores that there are massive amounts of data everywhere, including at WienIT, but data only has value when it is actually used. That becomes concrete when leadership teams need visibility into personnel numbers or annual budgets, or when operational teams need clarity on maintenance.

In the context of the Wiener Stadtwerke group, WienIT acts as a service provider. When Wiener Energie comes with power plant data, for example, the goal is to visualize the maintenance situation across plants. Hackl and her team provide storage for those datasets, process them, and deliver a report that enables practical use.

From her description we get a crisp picture: Lead Data Engineering at WienIT is about building the bridge from raw inputs to reliable insights for stakeholders whose decisions and services are essential to the city of Vienna.

End-to-end ownership: from ingestion to reporting

When Hackl speaks about leading projects, one word recurs: responsibility. As Tech Lead, everything technical passes across her desk: Which data do we pull? In what format and quality do we receive it? Which architecture and stack do we need? And how will it ultimately be reported? Behind every data flow is a series of decisions that must align technically and organizationally.

She acknowledges the job is demanding and responsibility-heavy — and very satisfying when it comes together. The reason is impact. The Wiener Stadtwerke deliver essential services. Clear, well-prepared data supports decisions that affect real-world infrastructure and service quality. That is a powerful motivator.

The essence: make data usable

Hackl compresses the core of her role into a simple idea: make data usable. She points out that there are huge amounts of data everywhere, including at WienIT, but that volume only becomes valuable when it is used. A clean pipeline embodies that idea:

• Ingest: clarify connections, formats, frequencies, and accountability.
• Process: clean, integrate, and model.
• Store: use cloud data storage and data warehouse structures for reliability and scale.
• Present: provide reports in Power BI that answer concrete stakeholder questions.

These are technical words, but the purpose is larger: visible, usable, decision-relevant information. Engineers who start with this outcome in mind naturally prioritize what helps their organization the most.

The stack: Azure and Power BI, used with focus

Rather than tool vanity, Hackl emphasizes a pragmatic pairing: Azure for the cloud, Power BI for reporting. She is accountable for shaping and guiding this stack across incoming projects. Whether the topic is HR, budgets, or power plant maintenance, the pipeline remains consistent while the questions vary.

For engineers, that is an invitation to stay outcome-driven: worry less about idealized tool debates and more about how the chosen cloud and reporting components translate a customer’s data into answers.

Tech Lead reality: mostly people, interfaces, and decisions

One of Hackl’s most striking lines is about work composition:

The rest is coordination with customers, collaboration with colleagues in other departments, alignment with other engineers, and documentation of working methods. In other words, all the orchestration that carries a data project from request to productive use.

For aspiring leads, this is the key insight: responsibility means uniting code and context. Meetings, threads, specs, and Q&A are not interruptions — they are the job. Technical excellence only becomes organizational effectiveness when it is coupled with communication, expectation management, and traceable decisions.

Paths into the field: university or self-taught — and then more

On the question of training, Hackl stays pragmatic. For Data Engineering, a technical education is useful. In Austria, university degrees remain popular. It is equally possible to learn the craft through courses and self-study. Both paths can work.

However, the lead role requires more than technical strength. She explicitly highlights organizational skills and people skills. The practical advice is to not shy away from responsibility but to seek it out: join in where you can, take charge of parts of a project, and grow into the role gradually. Over time you take on larger projects and more responsibility — eventually you become a Tech Lead.

It is an understated recommendation with a precise compass: leadership is not merely assigned; it is assumed through initiative and reliability.

Use cases that make the value tangible

Without venturing into confidential territory, Hackl’s examples illustrate recurring needs:

• Leadership requires visibility into personnel numbers.
• Annual budgets must be transparent and current.
• For Wiener Energie, power plant data supports assessment of maintenance across facilities.

What unites these scenarios is decision context. Reports are not visuals for their own sake; they are navigational tools for operations and strategy. When the pipeline works, it brings rhythm, transparency, and steering clarity.

Takeaways we learned from the session: five guiding principles

From Hackl’s account, we distilled guidance for data engineers — and those aiming to become one:

1) Follow the energy. The late spark before graduation fueled a decisive pivot. Energy beats timing.

2) Think end to end. Ingest, process, store, present — a pipeline is only as strong as its weakest link.

3) Build for use, not for elegance. Data has value only when it is used.

4) Treat responsibility as practice. Start with small ownership, deliver well, expand your scope.

5) Make people skills core. If only 30 percent is purely technical, the rest is coordination, communication, and documentation.

Practical moves: from contributor to lead

If you want to reach the next level, Hackl’s emphasis points to concrete actions:

• Make ownership visible: commit to deliverables, ship reliably, document outcomes, and share lessons learned.
• Shape interfaces early: talk to customers and adjacent teams, gather expectations, surface open questions, resolve ambiguities.
• Aim reporting at decisions: define the decisions each report should inform before building it.
• Let architecture follow the data flow: choose a stack based on input, transformation logic, and reporting needs.
• Treat documentation as a team service: make working methods traceable so success can be repeated.
• Deliver small beacons: share early report versions or models to invite feedback and build trust.

These steps are not flashy, but they compound. They make your work both visible and dependable — two attributes that define effective leads.

Why responsibility is worth it

Hackl is open about the workload and responsibility in her role — and equally clear about the satisfaction that follows solid delivery. Impact is the difference. The Wiener Stadtwerke provide essential services; improving the data foundation influences the quality of those services. In a data-driven setting, the biggest reward is seeing how structured information improves decisions.

The example of power plant maintenance captures this well. Transparency enables faster reaction, prioritization, and resource allocation, ultimately making systems more robust.

A starter playbook for aspiring data engineers

Hackl’s journey suggests a credible entry path:

• Find your entry moment: a workshop, a course, or the first look at a command line — any way to touch real tasks.
• Stick with it and iterate: code regularly, build small projects, experiment with ETL flows.
• Choose a foundation: Data Science or a technical degree, or structured self-study through courses.
• Train end-to-end thinking: look beyond scripts to understand how data reaches reporting.
• Learn reporting early: use Power BI or a comparable tool to internalize end-user needs.
• Practice communication: clarify requirements, summarize results, document decisions.
• Invite responsibility: lead small workstreams, manage dependencies, surface risks early.

This is not the only path, but it mirrors the movement Hackl describes — from technique to impact, from task to responsibility.

Quotes that encapsulate the role

Several statements from Hackl capture the role with crisp focus:

Together, they read like the DNA of modern data leadership: outcome-first, pipeline-minded, people-centric, and responsibility-driven.

What Azure and Power BI enable in this picture

Without diving into product minutiae, the combination of cloud storage and data warehousing on Azure with Power BI reporting addresses a wide range of business questions. It centralizes data flows, builds consistent models, and exposes results through a familiar front end. For leaders weighing personnel and budget decisions, reliability beats novelty.

For engineers, the message is similar: know your tools, but think in outcomes. A good stack is a means, not an end. What matters is whether it carries the questions the business needs answered.

From intent to reality: the practice of not shying away

Hackl puts it plainly: to step into this position you must be willing to take on a lot of responsibility. Not shying away is less a personality trait and more a practice. Start small, expand steadily, work transparently, seek feedback, and aim for the next level — that is her map for growing into a lead role.

This approach opens doors regardless of whether you enter through a classic university program or through courses and self-study.

For teams and organizations: what a Tech Lead enables

Beyond the personal story, the session illustrates the organizational value of tech leadership:

• Consistency across projects: a central view on sources, stack decisions, and reporting ensures compatibility.
• Clear accountability: routing crucial decisions through experienced technical leadership reduces fracture points.
• A living bridge: a lead translates between business requirements, engineering reality, and operations.
• Documented working methods: knowledge becomes structured and reusable.

In environments with many data sources and heterogeneous stakeholders, this role is essential to turn data silos into reliable information products.

A realistic role model for the career path

Hackl’s path is both atypical and textbook. Atypical because it began outside computer science. Textbook because the movement is so clear: discover enthusiasm, formalize it through study or courses, assume end-to-end responsibility, and grow step by step into the lead role.

That she tells this story with humor and visible enthusiasm is not an aside — it is the fuel. Responsibility demands effort. Feeling purpose and observing impact keeps you motivated in the long run.

Conclusion: a blueprint worth following

The session Michelle Hackl, Lead Data Engineer bei WienIT, with Speaker Michelle Hackl from WienIT distilled how technical excellence and organizational clarity come together. The pillars are clear:

• Data is only valuable when used.
• A Lead Data Engineer thinks end to end.
• The stack is a means to an end; Azure and Power BI play their roles reliably.
• Responsibility is the path into leadership — incremental, visible, repeatable.
• Thirty percent tech means seventy percent people and process.

For us at DevJobs.at, this is an encouraging narrative for anyone who started late, changed direction, or is just beginning to carry more responsibility. Hackl shows that timing is secondary when the direction is right. If you embrace the command line, keep the joy of programming alive, and lean into responsibility, you can shape an organization’s data reality — and help improve the services people rely on every day.

• 

  

  Florence Adegeye, Service Owner bei WienIT

  Service Owner bei WienIT Florence Adegeye gibt im Interview Einblicke in den Aufbau der Teams, worauf beim Recruiting der Fokus liegt und welche Technologien im Unternehmen Einsatz finden.

  Watch now

• 

  

  Paula Gludovatz, Scrummie bei WienIT

  Paula Gludovatz von WienIT erzählt im Interview über ihren Werdegang bis hin zur aktuellen Arbeit mit Scrum und was hier für Neueinsteiger wesentlich ist.

  Watch now
• 

  

  Max Ehlmaier, Full Stack Developer bei WienIT

  Max Ehlmaier von WienIT beschreibt im Interview wie er zum Full Stack Development gekommen ist, mit welchen Aufgaben er in seiner Arbeit zu tun hat und gibt Tipps für Einsteiger.

  Watch now