Liberty IT’s Sarah Whelan discusses the skills she uses daily and gives her reaction to her nomination as part of Liberty IT’s Culture Stars initiative.

“I’m a principal software engineer in the data space at Liberty IT, leading data pipeline enablement and experimentation to help product and analytics teams deliver reliable data and run faster experiments,” said Sarah Whelan. 

A working day might involve designing reusable patterns, templates and tooling while working across functions to improve observability, testing and delivery practices, according to Whelan, who is also involved in a company group designed for women in STEM. 

She told SiliconRepublic.com, “Alongside my day job, I co‑chair the Women in Tech employee group and mentor junior engineers, providing career guidance and technical coaching. 

“That work focuses on removing barriers through skills workshops, resources for career growth and forums where diverse voices can share experiences. The group runs mentoring circles, interview practice sessions and visibility events that create concrete opportunities and help normalise diverse career paths in engineering.”

If there is such a thing, can you describe a typical day at work?

My day balances technical tasks and collaboration. I’ll scan pipelines and deployment health first, address urgent alerts, then focus on code reviews. For me, reviews are an opportunity to mentor, surface better approaches and make our work more maintainable. I set aside time for architecture discussions and documenting decisions so future work is clearer.

I spend time working with our product teams to shape the roadmap, meet stakeholders to understand their problems and identify solutions, and coordinate with other teams to resolve dependencies. I also plan and run mentoring sessions and Women in Tech events, organising speakers, agendas and logistics.

What types of projects do you work on?

My work delivers dependable data platforms for analytics and machine learning. I build production-grade data pipelines that give teams reliable, well-instrumented datasets. To make delivery repeatable, I design experimentation frameworks, templates and patterns that reduce manual effort.

I focus on observability, testing and scaling so pipelines stay performant and lead enablement sessions that teach people how to use the tools and run experiments without heavy engineering support.

What skills do you use on a daily basis?

I use core data engineering skills every day: Python for transformations and orchestration, SQL for modelling and validation, and testing and monitoring to keep systems dependable. I pair that with careful, experimental thinking, small trials, metric tracking and incremental rollouts, so changes are low-risk and measurable.

On the people side of things, clear communication, active listening and regular collaboration help turn technical work into useful outcomes. I focus on creating easy pathways for success by mentoring colleagues, running pairing sessions for practical learning and producing simple playbooks that let teams self‑serve.

What is the hardest part of your working day

The hardest part is switching gears – going from fixing urgent production issues to design workshops or running hands‑on pairing sessions can really break your flow. I try to make it easier by agreeing priorities with the team, protecting blocks for focused work and keeping documentation up to date so I can pick up where I left off. Quick handovers and regular check‑ins also keep longer‑term work visible.

Do you have any productivity tips that help you through the working day?

I use a to-do list to track outstanding tasks and review it each morning to plan and prioritise my day. I block focused time in my calendar for heads‑down work, which helps me avoid context switching. I document everything in a central, easily accessible location so the team never has to ‘figure something out’ twice. I also make mentoring a recurring calendar item, so coaching happens regularly.

When you first started this job, what were you most surprised to learn was important in the role?

I was surprised by how much context and communication matter; technical solutions alone rarely succeed without stakeholder buy‑in and agreed processes. I also didn’t expect observability and experiment rigour to be so central. Good monitoring, testing and repeatable experiment practices are what make pipelines reliable in production.

Finally, the value of documentation and small, consistent practices (like decision logs and runbooks) became obvious fast – they save time and prevent firefighting.

How has your role changed as the sector has grown and evolved?

The arrival of generative AI has raised the bar; it requires high‑quality, well‑labelled data, feature management, stronger data contracts and privacy controls, plus new inference and embedding pipelines and model observability, which makes the role more strategic and cross‑functional. At the same time, there’s a steady stream of new tools and platforms, so a crucial skill is distinguishing genuinely useful technology from marketing hype and choosing tools that solve real problems.

What do you enjoy most about the job?

I enjoy making things better for the people I work with. Most of my role is about simplifying data delivery so users get reliable, timely datasets and can make decisions faster. Each day, I try to keep the team unblocked, staying on top of potential issues so colleagues can get on with their day‑to‑day work with minimal friction.

What I like most about the job is knowing my work makes other people’s lives easier, whether that’s a data user getting answers faster or a teammate having one fewer thing to worry about. I also enjoy helping others build skills and confidence, and access opportunities. Practically, that looks like one‑to‑one coaching, structured pairing sessions and setting up repeatable playbooks so people can succeed without constantly relying on one person.

I often run knowledge‑sharing sessions or demos to share what I’ve learned and get feedback. It’s great to see patterns I’ve created adopted by other teams. When I notice incremental improvements or hear someone say a change saved them time, it reminds me why this work matters.

You received a nomination as part of Liberty IT’s Culture Stars initiative – tell us more about what this nomination meant to you?

The nomination in the ‘Be Brilliant’ category recognised mentorship, teamwork and pragmatic technical leadership. Seeing my mentee secure a promotion was the proudest, most concrete outcome; it showed the real, human impact of focused coaching and regular feedback.

The nomination also acknowledged the everyday teamwork and practical improvements I champion to make our pipelines more reliable. Being recognised was validation that consistent, sometimes unglamorous work – supporting others, documenting decisions and removing roadblocks – does make a difference. 

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