Introduction

Data and AI jobs often feel like a messy deck of cards. Every month, a new role gets added or merged to the mix. And suddenly, you’re left wondering: what’s the real difference between a Data Scientist, a ML Engineer, and an AI Engineer? And when did the Data Analyst and the Data Engineer merge into a new card, the Analytics Engineer…

Each role comes with its own power, a seat at the table, and a market value. In this guide, we reshuffle the organizational chart into a poker game, so you can build the best possible hand for your strategy, your team size, and your stage of maturity. Ready to decode the cards you’ll actually play? 🎴

Shuffling the deck 🔀

Before we dive into the suits and the cards, let’s pause for a moment. We need to understand why the cards keep changing, and why faster every year.

In my opinion, they are three forces keep shuffling the deck:

• Stack shifts: warehouses, lakehouses, and now vector databases have changed how we store and serve data.
• Product mindset: from one-off dashboards to data products, roles have moved closer to outcomes.
• The Gen AI wave: with patterns such as LLMs with retrieval-augmented generation, media generation, and model wrappers, it has introduced new skills and roles..

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The Four Suits

If the deck keeps getting constantly reshuffled, it’s easy to get lost in the buzz of new titles and shifting roles. Without structure, it all turns into noise, and teams argue about words and not values. That’s why we split the roles into four suits, each with its own clear place at the table.

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♥️ Strategy and Governance

In poker, before anyone plays a hand, the rules must be clear and the table must be trusted. In the Data & AI world, this suit brings order, alignment, and credibility. These roles decide how the game is played, what counts as a win, and how to keep players accountable.

#CDO/CAIO – Chief Data / AI Officer: The Ace of Hearts

• Role: defines the company’s Data & AI strategy, operating model, governance, and ROI.
• Powers: executive alignment, portfolio prioritization, budget ownership, org design, and policies.
• Tools: roadmap, data catalog, DAMA practices, compliance frameworks.
• Proportion: 1 per company (or 1 per BU in very large groups).
• Market value: 🔹🔹🔹🔹🔹

💡 Real-life: The job title varies with context. Some companies go “CDO” to stress governance and data value, others go “CAIO” to stress AI adoption. In practice, both hats can cover the same responsibilities, just with a different emphasis.

#DGL – Data Governance Lead: The King of Hearts

• Role: implements governance, quality, security, compliance, catalog.
• Powers: stewardship model, SLAs, lineage, privacy handling.
• Tools: Collibra, Alation, Purview, quality frameworks.
• Proportion: 1-2 per company or per BU.
• Market value: 🔹🔹🔹🔹

💡 Real-life: The need for this role is rising fast with regulatory pressure. Frameworks like GDPR in Europe, BCBS239 in banking (Europe), or privacy laws like the California Consumer Privacy Act (CCPA) make governance not just best practice, but a legal requirement.

#DST – Data Steward: The Jack of Hearts

• Role: domain-level owner ensuring definitions, quality, and usability.
• Powers: dictionary, quality rules, access reviews, semantic arbitration.
• Tools: data catalog, anomaly detection, MDM.
• Proportion: 1–2 per data domain (customer, product, finance).
• Market value: 🔹🔹🔹

💡 Real-life: For example, in a domain like customer, “active customer” might mean different things in marketing, sales, or finance. The Data Steward ensures one consistent definition.

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♦️ Platform and Architecture

No game without a table, no bets without poker chips. In Data & AI, this suit lays the technical foundation: how data is stored, moved, secured, and made available. These roles ensure that the platform is solid, scalable, and cost-efficient so the rest of the players can focus on winning hands.

#DAR – Data Architect: The Ace of Diamonds

• Role: designs architecture, patterns, and standards, balancing cost with performance.
• Powers: chooses warehouse, lake, or lakehouse; defines security and integration models.
• Tools: AWS, GCP, Azure, Hadoop; Warehouses, Lakes, Lakehouses; Parquet, Delta; IAM.
• Proportion: 1–2 per platform.
• Market value: 🔹🔹🔹🔹🔹

💡 Real-life: In some organizations, a Data Modeler supports the Data Architect. The Architect sets the big picture (platform, integration, governance), while the Modeler focuses on logical and conceptual models: turning business entities into schemas the Architect embeds in the overall design.

#DE – Data Engineer: The King of Diamonds

• Role: builds and industrializes data pipelines, batch and streaming.
• Powers: ingestion, transformation, orchestration, reliability, and cost control.
• Tools: dbt, Airflow, Fivetran, Spark, Kafka, SQL, Python.
• Proportion: 3–8 per product or platform.
• Market value: 🔹🔹🔹🔹

💡 Real-life: Data is spread across many BUs and systems (CRM, ERP, finance, ops). The Data Engineer is the backbone of the team, stitching everything together — which makes it the most common role in the deck.

#D(ML)Ops – Data/ML Ops Engineer: The Jack of Diamonds

• Role: ensures reliability, deployment, and observability of the platform.
• Powers: CI/CD, data testing, monitoring, FinOps.
• Tools: Terraform, GitHub Actions, Docker, Kubernetes, Prometheus, Grafana.
• Proportion: 1–2 per platform.
• Market value: 🔹🔹🔹🔹

💡 Real-life: This is the scale-up card. It’s what takes you from a notebook demo to a model that real customers can use every day. Without DataOps/MLOps, pipelines break, models rot, and AI stays stuck in experiments instead of creating business value.

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♠️ Analytics and Product

In poker, the best players don’t just look at their own cards, they read the table, the odds, and the story unfolding. In Data & AI, this suit plays exactly that role: turning questions into insights, raw data into reliable products, and analyses into decisions.

#D(I)PM – Data/IA Product Manager: The Ace of Spades

• Role: drives data products from discovery to run, maximizing value and adoption.
• Powers: discovery, backlog, prioritization, experimentation, go-to-market.
• Tools: SCRUM, SAFe, roadmap, usage tracking, documentation.
• Proportion: 1 per data product, or 1 for 2–3 smaller ones.
• Market value: 🔹🔹🔹🔹

💡 Real-life: Titles vary a lot across companies. Sometimes this role is called Product Owner or even Project Manager. The labels change, but the mission stays the same: ensure that data initiatives deliver clear business value.

#AE – Analytics Engineer: The King of Spades

• Role: bridges Data Engineer and Data Analyst, delivering analytics-ready data.
• Powers: semantic modeling, centralized metrics, reliable datasets.
• Tools: SQL, dbt, semantic layers, BI tools.
• Proportion: 1–3 per domain.
• Market value: 🔹🔹🔹🔹

💡 Real-life: This role popped up to solve a recurring frustration: Data Analysts waiting endlessly for usable data, and Data Engineers building pipelines without understanding business context. The Analytics Engineer closes that gap, creating reliable, business-ready datasets that both sides can actually use.

#DA – Data Analyst: The Jack of Spades

• Role: turns business questions into actionable answers.
• Powers: exploration, visualization, KPI design, storytelling, A/B tests.
• Tools: SQL, Power BI, Tableau, Looker, light Python or R.
• Proportion: 2–10 per BU depending on demand.
• Market value: 🔹🔹🔹

💡 Real-life: The definition of Data Analyst can change widely. In some companies, it’s a technical BI profile with SQL and visualization skills. In others, it’s closer to a Business Analyst, lighter on data, stronger on business context.

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♣️ ML and AI in Production

In poker, once the basics are mastered, players start pulling advanced tricks: bluffing, reading micro-signals, or optimizing bets. In Data & AI, this suit is about the technical experts who push beyond reporting into predictions, optimizations, and now generative AI.

#DS – Data Scientist: The Ace of Clubs

• Role: builds predictive, prescriptive, or generative models for focused use cases.
• Powers: feature engineering, experimentation, evaluation, ML framing.
• Tools: Python, notebooks, sklearn, XGBoost, LLM tooling, MLflow.
• Proportion: 1–3 per AI product.
• Market value: 🔹🔹🔹🔹

💡 Note: In many companies, Data Scientist is a fuzzy card. Sometimes it means “fancy Data Analyst” (SQL, dashboards, light stats). Other times, it means a PhD-level researcher building models in notebooks with no production path. This ambiguity is exactly what created the need for new cards.

#MLE – Machine Learning Engineer: The King of Clubs

• Role: deploys to production, optimizes latency, cost, and robustness.
• Powers: model serving, real-time features, GPU/CPU optimization.
• Tools: APIs, Triton or TF Serving, Feast, Faiss, pgvector, Docker, Kubernetes.
• Proportion: 1–3 per AI product.
• Market value: 🔹🔹🔹🔹

💡 Note: The role of MLE emerged in the early 2020s as a response to the “Data Scientist gap”. Companies realized that prediction models (forecasting, scoring, optimization) needed dedicated engineers to make them reliable, fast, and cost-efficient in production. The MLE is the pragmatic counterbalance to the DS’s research focus.

#AIE – AI Engineer: The Jack of Clubs

• Role: builds and integrates AI systems, especially around LLMs and agentic workflows.
• Powers: retrieval-augmented generation (RAG), prompt design, evaluation pipelines, agent orchestration.
• Tools: LangChain, LlamaIndex, vector databases, orchestration frameworks, evaluation suites.
• Proportion: 1–3 per AI product, depending on scope.
• Market value: 🔹🔹🔹🔹

💡 Note: The AI Engineer card emerged with the rise of large language models. Data Scientists were often too research-focused, and ML Engineers were more concerned with deployment of classic predictive models (forecasting, scoring, optimization). But with LLMs came new challenges: chaining prompts, building wrappers, managing vector databases, and deploying agentic systems.

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Overview of all the cards

We’ve walked through the four suits, but sometimes you just want to see the whole deck at a glance. Here’s the big picture !

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Build your hand

In poker, winning is rarely about holding a single magic card. It’s about playing the right combination of cards for the situation. Data & AI teams work the same way: the best hand depends on your company size, maturity, and goals.

Let’s lay down three example hands:

1️⃣ Starter hand – Business Intelligence first for Small and Medium Businesses (SMBs)

• ♥️ Data Product Manager (1 card)
• ♦️ Data Engineer (1 card)
• ♠️ Analytics Engineer (1-2 cards)
• ♠️ Data Analyst (1–2 cards)

2️⃣ Scale hand – Modern Data Platform

• ♥️ Chief Data Officer (1 card), ♥️ Data Gouvernance Lead (1 card)
• ♦️ Data Architect (1 card), ♦️ Data Engineer (3 cards), ♦️ DataOps / MLOps (1 card)
• ♠️ Data Product Manager (1 card), ♠️ Analytics Engineer (2–3 cards), ♠️ Data Analyst (3–6 cards)

3️⃣ AI in production hand – Advanced Play

• ♥️ Chief Data/IA Officer (1 card), ♥️ Data Gouvernance Lead (2 cards), ♥️ Data Steward (3 cards)
• ♦️ Data Architect (2 cards), ♦️ Data Engineer (5 cards), ♦️ DataOps / MLOps (2 cards)
• ♠️ Data Product Manager (3 cards), ♠️ Analytics Engineer (2 cards), ♠️ Data Analyst (3 cards)
• ♣️ Data Scientist (2 cards), ♣️ Machine Learning Engineer (2 cards), ♣️ AI Engineer (2 cards)

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Conclusion

You’ve got to know when to hold ’em,
Know when to fold ’em,
Know when to walk away,
And know when to run.

Kenny Rogers – The Gambler

If you don’t know this song, seriously, go listen to it — it’s a masterpiece. 🎧

And here’s the twist: in poker as in Data & AI, once you understand the cards, the suits, and how to play your hand, the win is no longer about luck. You already know how to play to win 🃏🚀 !

👉 You’ve now seen the poker cards, the roles that make up a Data & AI team. But cards are nothing without the right tools on the table. Curious about the tooling side? Data Team Maturity: Understanding Tooling Levels.