Data Usage and Job Functions

Data is more than just one job; it encompasses an entire ecosystem of professions that convert raw data into judgements, forecasts, and intelligent goods. In this tutorial, we'll look at the specialised positions whose primary craft is data.(Of course, many other occupations rely on data or machine learning, including marketers, physicians, operators, and product leaders. However, the professions listed below are data-driven; they are careers that you may directly pursue if you are studying or working in the field.)

Data Collection & Infrastructure

The foundation layers which includes sources, pipelines, storage, and serving, which allow the rest to exist. This category includes the pipelines, databases, and storage systems that capture and transport data. It allows all other teams to obtain accurate, timely, and well-structured data. Without a strong infrastructure, data-driven organisations cannot securely process millions of rows of data or maintain compliance with data-handling standards.

Data Engineer: Builds ingestion, transformation, and orchestration (Airflow/dbt/Spark); optimizes cost/performance.

Education: Computer Science/Data Engineering

Database Administrator (DBA): Ensures database performance, security, backup/restore.

Education: Computer Science/Information Science

Data Architect: Designs end-to-end data platforms (lakes/warehouses), standards, and integration patterns (batch/stream).

Education: Computer Science/Cloud Architecture

Data Preparation & Management

Making data reliable, accessible, and compliant. Without this layer, analysis and AI are based on sand. This function guarantees that data is clean, consistent, and compliant prior to any analysis or modelling. It is data-centric because it converts raw, unstructured data into dependable inputs for business intelligence and machine learning. It is important to safeguard decision quality, poor data governance leads directly to erroneous credit models, faulty reporting, and regulatory risk.

Data Steward / Data Governance Specialist: Owns data definitions, access, quality rules, lineage, and compliance (e.g., GDPR/AI Act).

Education: Information Systems, Business Administration, or Data Governance.

ETL / Integration Developer: Moves and reshapes data via pipelines (batch/stream), ensuring comparability across systems.

Education: Computer Science, Data Engineering.

BI Developer: Models data for reporting and builds performant, governed semantic layers and dashboards.

Education: Computer Science/Business Analytics/Information Systems

Data Analysis & Insights

Transforming questions into answers. These jobs investigate, summarise, visualise, and explain what occurred and why. Analysts are at the forefront of evidence collection; they build a shared understanding of reality that leaders may use to take action. Analysts utilise statistical reasoning, visualisation, and business logic to uncover patterns that motivate action.

• Data Analyst: Queries and analyses datasets, builds dashboards, and tracks KPIs to inform decisions. Strong in SQL, Excel, and BI (e.g., Power BI, Tableau); often light Python/R.

Education: Data Analytics, Statistics, Economics, or Business Analytics.

• Risk Analyst: Specializes in credit, market, or operational risk. Evaluates creditworthiness, loss drivers, and portfolio exposure; recommends limits, pricing, or policies.

Education: Finance, Economics, Statistics, Risk Management; FRM/CFA are valued. Why it matters: balances growth and safety.

• Business Analyst / Product or Marketing Analyst: Translates business goals into metrics and analyses; runs experiments and segmentation.

Education: Business/Economics/Marketing Analytics.

Advanced Analytics & Predictive Modelling

Using statistics and machine learning, we can turn past into foresight. It's the most data-intensive layer, needing strong numerical and computational knowledge to anticipate outcomes and measure uncertainty.

• Data Scientist: Designs experiments, features, and models; explains drivers; partners with the business to deploy usable solutions.

Education: Data Science, Statistics, Computer Science, Applied Math

• Machine Learning Engineer: Productionizes models (APIs, monitoring, CI/CD), optimizes latency/reliability, and scales inference.

Education: Computer Science/Machine Learning

• Quantitative Analyst (Quant): Builds stochastic, time-series, or optimization models for trading, pricing, and risk.

Education: Math, Statistics, Physics, Finance

AI & Decision Systems

Developing intelligent systems that can recommend, classify, or determine in real time. It combines live data sources, machine learning, and business logic to help make consistent, adaptable judgements.

• AI Engineer / Applied AI Scientist: Uses deep learning/LLMs, vector search, and NLP/CV to create production features (search, scoring, summarization). Education: CS/AI/Data Science.
• Decision Scientist: Designs policy logic, causal inference, and uplift strategies to optimize decisions (e.g., approvals, limits, collections). Education: Statistics/Operations Research/Econometrics.
• Data Product Manager: Owns the strategy, roadmap, and success metrics for data/AI products; aligns technical feasibility with business value. Education: Business/IS/CS hybrid.

Specialized & Emerging Roles

The cutting edge is where scale, safety, and innovation meet. Extending the ecosystem to include automation, fairness (Ethics), and long-term strategy. Their significance is in sustaining the future of responsible AI, ensuring models stay explainable, compliant, and consistently helpful as organisations grow.

• MLOps Engineer: Unifies Dev + Ops for ML: automated training, testing, deployment, monitoring, rollback, and governance of models. Education: CS/ML; DevOps experience.
• Data Privacy / Ethics Specialist: Designs privacy-by-design, fairness testing, and consent/purpose controls; audits proxies and disparate impact. Education: Law/Ethics/IS Governance.
• Research Scientist (AI/ML): Advances methods (e.g., sequence models, causal ML); prototypes become future products. Education: PhD-track CS/Math/AI.
• Data Strategy Consultant: Aligns data investments to outcomes; creates roadmaps, operating models, and value cases.

How These Job Functions Work Together

Think about the flow as follows: Collection & Infrastructure collects and saves data. Preparation and management make it reliable and compliant. Analysis and Insights outlines what happened. Advanced analytics anticipates what might happen.AI and decision systems automate tasks, while specialised roles ensure fairness, scalability, and safety.

Choosing Your Path

Careers are not silos; they are journeys. Many people begin in analysis, specialise in risk management, and then advance to data science or decision science. Others progress from data engineering to architecture. Governance professionals frequently rise to Head of Data/CDO positions. Wherever you begin, keep three habits:

• Learn the business. Domain expertise multiplies your impact.
• Own quality. Great data beats fancy models.
• Explain your choices. If you can’t explain it, you can’t defend it.

Why Each Role Matters: A Day in the Life of a Data-Driven Bank

Every decision at NordFinance, a prominent European consumer bank, is fuelled by data. When a group of data analysts notices an unexpected increase in late payments among variable-income customers, risk analysts investigate further, connecting the behaviour to economic data and exposure trends. Data stewards and BI developers work behind the scenes to keep information clean and dashboards reliable, ensuring that insight is grounded in reality. Their discovery prompts the data science team to create a prediction model that estimates who will default next month. Machine learning engineers implement it, while quants convert the model's outputs into capital and profitability terms.

The new model feeds into an AI-powered decision system overseen by AI engineers and decision scientists, who fine-tune credit-approval criteria to safeguard good consumers while warning problematic ones. Data engineers, DBAs, and architects manage the pipelines that feed new, secure data every hour, while MLOps and ethics professionals ensure fairness, drift, and compliance. What began as a few missing payments becomes a concerted effort across disciplines, proving that when every function in the data ecosystem works together, information becomes foresight, and foresight becomes responsible, data-driven action.

A final nudge

The purpose isn't to "work with data."Your objective is to generate better outcomes, safer loans, more equitable decisions, and smarter goods.Choose the role that allows you to accomplish it best, and then develop the abilities (and portfolio) to demonstrate it.

In subsequent articles, we will delve into each of these roles.Their skill needs are also presented, along with appropriate resources for beginning to master the requisite skill sets.