Data Jobs Decoded: Finding Your Fit in the New Data Landscape

In the evolving world of data, newer roles like AI engineers and prompt designers are capturing attention, but it’s important not to overlook the essential foundation roles that still power the entire data ecosystem. Data engineers, data wranglers, and data architects remain in high demand — they're the ones who build and maintain the infrastructure, clean and prepare data for use, and design scalable systems that allow organisations to trust and leverage their data effectively. These roles continue to be the backbone of any data team and are often critical stepping stones into more specialised or senior roles.

Last year I shared a blog post in Getting Started in a data career “From Intern to Data Scientist: Getting Started in a Data-Driven Career”, I shared tips for getting your first foot in the door as a data professional. A few years on, it’s time for an update — not just because the tools have changed, but because the job titles have too.

The data space has expanded rapidly, and roles are now more specialised (and sometimes more confusing!). Whether you’re new to tech, switching roles, or mentoring someone just starting out, it’s useful to understand what these roles involve and where they fit. I’ve also included the SFIA skills associated with each to help those using the Skills Framework for the Information Age to map out their capability growth.

Here’s a quick tour through some of the most common data career paths today and where they sit in the ecosystem:

1. Data Analyst

The translators of raw data.
Data analysts work with existing datasets to produce insights, trends, and visualisations that support business decisions. They’re often the ones building dashboards, reporting KPIs, and answering business questions like “what’s happening and why?”

Common skills: SQL, spreadsheets, statistics, data visualisation
Tools: SQL, Excel/Sheets, BI tools
Getting started: Learn Excel/Sheets and SQL first — these are your foundations. Then move onto BI tools eg: Power BI, Tableau Public Quicksight, QlikSense, Metabase, Mode, Redash.

🔹 SFIA skills:

• DATM (Data Management)

• DAAN (Data Analysis)

• VISL (Information Visualisation)

• REQM (Requirements Definition and Management)

2. Data Scientist

The predictors and modelers.
A data scientist applies machine learning and statistical techniques to derive deeper insights or make predictions from data. They work at the edge of what’s possible with data — whether that’s customer segmentation, fraud detection, or AI models.

Common skills: Python/R, machine learning, statistics, data wrangling, feature engineering
Tools: Python, Excel, Tableau
Getting started: Learn Python and get hands-on with libraries like pandas, scikit-learn, and seaborn. Try a beginner Kaggle competition.

🔹 SFIA skills:

• DASV (Data Science)

• MLRG (Machine Learning)

• DBDS (Data Engineering)

• RSCH (Research)

• DAAN (Data Analysis)

3. Business Analyst

The bridge between people and data.
Business analysts sit at the intersection of business and tech. They document processes, capture user requirements, and support teams to deliver tech-enabled solutions. It’s a people-focused role grounded in analytical thinking.

Common skills: Process modelling, communication, stakeholder engagement, light data analysis
Tools: Office / office tools, Excel/Sheets, Chart tools (Lucid, Visio etc), Stakeholder collaboration tools (Miro, Mural etc)
Getting started: Learn the art of asking great questions - listening is key to success in this role. Get familiar with use cases, business process diagrams, and user stories.

🔹 SFIA skills:

• BUAN (Business Analysis)

• REQM (Requirements Definition and Management)

• BPRE (Business Process Improvement)

• RLMT (Stakeholder Relationship Management)

4. Machine Learning (ML) Engineer

The builders of smart systems.
ML Engineers are part coder, part data scientist, part DevOps. They scale machine learning models for production — making sure they’re performant, maintainable, and cost-effective.

Common skills: Python/Java, ETL and pipelines, ML deployment, software engineering
Tools: Spark, Hadoop, TensorFlow, Docker, Kubernetes, Flask. Every cloud platform ML toolkit - Google, AWS, Microsoft.
Getting started: A strong grounding in software engineering helps or Data Engineering. Explore MLOps tools and learn how to operationalise models.

🔹 SFIA skills:

• MLRG (Machine Learning)

• SENG (Software Engineering)

• DBDS (Data Engineering)

• ASUP (Application Support)

• TEST (Testing)

5. Generative AI (GenAI) Engineer

The frontier role.
GenAI engineers work on the cutting edge of AI, building systems that generate content, code, or insights using large language models and prompt engineering. The field is experimental, evolving fast, and full of opportunity.

Common skills: Python (Transformers, LangChain), prompt engineering, model fine-tuning
Tools: Hugging Face, LLAMA, LangChain
Getting started: Understand how foundation models work. Experiment with open-source models and learn responsible use.

🔹 SFIA skills:

• MLRG (Machine Learning)

• RSCH (Research)

• INOV (Innovation)

• EMRG (Emerging Technology Monitoring)

• TECH (Technology Strategy and Planning)

Closing Thoughts

While these roles offer different paths, they’re all part of the same ecosystem — turning data into insights, decisions, predictions, outcomes and products. Don’t be put off by the technical jargon or job titles; your career might move sideways, merge two of these roles, or evolve into something that doesn’t exist yet - and believe me we don’t know what the near term let alone long term future holds.

Using a framework like SFIA can help you assess where you are now and what capabilities to grow next — whether you’re a job seeker, manager, or educator. And if you’re thinking about taking your professionalism further, consider working toward Chartered IT Professional (CITP) certification - with specialisation certifications coming this year including data pathways. It’s a global benchmark that recognises your experience and professional standards — and ITP can help you assess your eligibility.

Still not sure where to start? Join a community like IT Professionals NZ, and connect with others in the same boat. We’re all learning together — and in data, that never stops.