
Mark Grebler
Head of Engineering
Focus HQ
location_on Australia
Member since 2 years
Mark Grebler
Specialises In
Mark has been working in software development for the last 15 years across a wide variety of industries such as Defence, Health, Emergency Management, Construction and Renewable Energy. He has been leading teams for the last ten years, and focusses his learning around leadership and getting the most out of teams.
He is currently Head of Engineering at Focus HQ.
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Designing a DevOps Dependency Diagram to Decide Development Direction
45 Mins
Case Study
Beginner
So you walk into a new company, get the lay of the land and then realise, crap! Their development processes are like they were design by a bunch of first-year uni students doing a group project.
There is no DevOps to speak of. There are snowflake servers everywhere. Their git branching strategy is unmanageable. They run tests only every 3 or 4 releases. Their deployment is manual and different for each release. The have no real alerting.
Ok. Take a deep breath! Calm down.
So much to do, but where to start? The business has produced a list of improvement actions, but those actions are focussed around fixing the symptoms of the problems, not solving the root cause. The business does not understand that the path to DevOps improvement is complex and each task has many inter-relations and dependencies.
This is the problem that I faced about a year ago. To overcome this, we went through a process of defining all of the DevOps tasks we could think of and mapped them into a dependency diagram. This diagram was useful to communicate both internal and external to the team.
In this case study, I’ll go through the process to design the dependency diagram, but also our progress through the diagram one year later.
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High performing software engineering teams: how to grow then and how to slow them
30 Mins
Talk
Beginner
This presentation will have a close look at what makes high performing software development teams, as well as what hinders them. It will cover each level of the organisational hierarchy starting at individual software developer, then group of engineers, full cross-functional product-engineering team, wider product-engineering department, and finish at the entire company. At each level, we will see multiple examples of teams to see what factors contribute to high performing software teams, as well as less performant teams.
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Unblock your learning: the neuroscience of learning and change
Lay CloughSr. Product OwnerMedAdvisorMark GreblerHead of EngineeringFocus HQschedule 1 year ago
Sold Out!90 Mins
Workshop
Beginner
Have you ever learnt something that, at the time, you thought would be life-changing, only to look back at yourself a year or so later and realise that you hadn't changed in the slightest? Why is that? Why is change so hard?
In this workshop which was presented at 1st Conference, we will show some of the neuroscience behind learning and change and explain why change is so hard, and provide some concrete techniques about how to learn more effectively and change your behaviours to put those learnings into practice.
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Does Agile culture discriminate against the neurodiverse
Mark GreblerHead of EngineeringFocus HQLay CloughSr. Product OwnerMedAdvisorschedule 1 year ago
Sold Out!45 Mins
Interactive
Beginner
As organisations have become more agile and try to build “high-performing” teams, they have started to hire for cultural and team fit. As a result they search for people who can collaborate and are team players; who are willing to develop a deep trust in each other and in the team’s purpose; who freely express feelings and ideas; who engage in extensive discussion; who are adaptable and embrace change and who are comfortable managing constructive conflict towards a better outcome.
There are however, many people who do not meet that criteria, and for various reasons, may struggle to meet that criteria in the future.
Everyone brain is wired slightly differently and not everyone’s wiring fits the mould described above.
Does the exclusivity of the above criteria result in certain people missing out who could have significant positive value.
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Does Agile culture discriminate against the neurodiverse
Mark GreblerHead of EngineeringFocus HQLay CloughSr. Product OwnerMedAdvisorschedule 1 year ago
Sold Out!45 Mins
Interactive
Beginner
As organisations have become more agile and try to build “high-performing” teams, they have started to hire for cultural and team fit. As a result they search for people who can collaborate and are team players; who are willing to develop a deep trust in each other and in the team’s purpose; who freely express feelings and ideas; who engage in extensive discussion; who are adaptable and embrace change and who are comfortable managing constructive conflict towards a better outcome.
There are however, many people who do not meet that criteria, and for various reasons, may struggle to meet that criteria in the future.
Everyone brain is wired slightly differently and not everyone’s wiring fits the mould described above.
Does the exclusivity of the above criteria result in certain people missing out who could have significant positive value.
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High performing software engineering teams: how to grow then and how to slow them
30 Mins
Talk
Beginner
This presentation will have a close look at what makes high performing software development teams, as well as what hinders them. It will cover each level of the organisational hierarchy starting at individual software developer, then group of engineers, full cross-functional product-engineering team, wider product-engineering department, and finish at the entire company. At each level, we will see multiple examples of teams to see what factors contribute to high performing software teams, as well as less performant teams
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Big Data in a production environment: Lessons Learnt
45 Mins
Talk
Intermediate
Big Data. It's complicated, right?
For the past 18 months or so, we've been developing a Big Data system that is used in production as part of our user facing application. I assumed this was the standard thing to do with Big Data and that there wouldn't really be much of a difference between using Big Data in user-facing production compared to using it for Data Science and Machine Learning.
As time passed I interviewed people for roles in our team, and talked to other teams, I found that the types of things that we were doing seemed to be different from what most other people were doing.
It may sound obvious, but it took me a while to work out that Big Data for Data Science requires different techniques to Big Data for user-facing products.
In this talk, I'll walk through what we're currently doing to solve our transformation problems, and explore some of the differences between user-facing and Data Science. I'll present some of the lessons we've learnt, the tech we've played with and invite you to help with some of the problems we continue to face.
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No more submissions exist.