SKILL.md

name	hr-data
description	Help HR managers, recruiters, and talent acquisition teams understand Data Engineering, Data Analytics, Data Science, Business Intelligence, Machine Learning, and modern data ecosystems. Use when asked to explain data roles, screen data engineers or data scientists, understand analytics workflows, compare data engineering and data science, evaluate data skills, create data interview questions, understand AI and machine learning teams, or any data and analytics hiring and recruiting task.
metadata	author version Tuan Duc Tran 1.0.1

HR data engineering hiring

Comprehensive Data and Analytics knowledge for HR and recruiters — from understanding modern data ecosystems and AI workflows to evaluating data candidates, interpreting portfolios, and improving technical hiring decisions.

Supported tasks

• Explaining data and analytics concepts for non-technical recruiters
• Understanding modern data ecosystems and AI workflows
• Screening Data Engineers, Data Analysts, and Data Scientists effectively
• Evaluating data portfolios, dashboards, Kaggle projects, and GitHub repositories
• Creating data interview questions and hiring scorecards
• Comparing Data Engineering, Analytics, BI, Data Science, and Machine Learning roles
• Understanding data pipelines, warehousing, and analytics workflows
• Identifying data seniority levels and skill expectations
• Understanding AI, machine learning, and modern data infrastructure
• Writing data-related job descriptions and hiring requirements
• Explaining data terminology used by engineers and analysts
• Understanding collaboration between data, product, engineering, business, and leadership teams

What data engineering and analytics mean in 2026

Modern data work is no longer:

• "just making reports"
• "only writing SQL queries"
• "just training AI models"

In 2026, modern data ecosystems increasingly include:

• cloud-native data platforms
• real-time analytics
• machine learning infrastructure
• AI engineering
• vector databases
• data governance
• observability
• analytics engineering
• large language model (LLM) workflows
• business intelligence automation

Modern data teams are increasingly expected to support:

• product decisions
• AI systems
• forecasting
• automation
• experimentation
• operational analytics
• executive reporting

AI-driven analytics and LLM-integrated workflows continue reshaping modern data teams.

Data ecosystem (2026)

Data processing and analytics

• SQL
• Python
• R
• Pandas
• Polars

Data engineering and pipelines

• Apache Spark
• Kafka
• Airflow
• dbt
• Dagster

Data warehouses and lakehouses

• Snowflake
• BigQuery
• Databricks
• Redshift
• ClickHouse

Lakehouse architectures continue growing due to unified analytics and AI workflows.

Business intelligence and visualization

• Tableau
• Power BI
• Looker
• Metabase
• Apache Superset

Machine learning and AI

• Scikit-learn
• TensorFlow
• PyTorch
• Hugging Face
• LangChain

Modern AI and vector ecosystems

• OpenAI APIs
• Vector databases
• Pinecone
• Weaviate
• ChromaDB

Cloud and infrastructure

• AWS
• Google Cloud Platform (GCP)
• Azure

Types of data-related roles

Data Analyst

Focuses on:

• reporting
• dashboards
• SQL analysis
• business insights
• KPI tracking

Business Intelligence (BI) Analyst

Focuses on:

• executive reporting
• dashboards
• business metrics
• data visualization
• operational insights

Data Engineer

Focuses on:

• data pipelines
• ETL/ELT workflows
• data infrastructure
• warehousing
• scalability

Analytics Engineer

Focuses on:

• transforming raw data into analytics-ready models
• dbt workflows
• business-facing datasets
• data quality
• metric consistency

Analytics Engineering continues growing rapidly between traditional data engineering and analytics teams.

Data Scientist

Focuses on:

• predictive modeling
• experimentation
• statistical analysis
• machine learning
• forecasting

Machine Learning Engineer

Focuses on:

• deploying ML systems
• ML infrastructure
• production AI pipelines
• model serving
• scalability

AI Engineer

Focuses on:

• LLM applications
• AI product integration
• retrieval systems
• prompt engineering
• AI infrastructure

AI Engineering has become one of the fastest-growing technical roles in modern software organizations.

Key prompts

Data fundamentals

1. "Explain data engineering, analytics engineering, and data science in simple terms for [non-technical sourcers]."
2. "What does a [Data Engineer] actually do day to day in a [product vs data-infrastructure team]?"
3. "What is the difference between [Data Analyst, Data Engineer, Data Scientist, and AI Engineer]?"
4. "Why are modern companies investing heavily in [scalable cloud data lakehouses]?"
5. "What data and analytics skills are most important in hiring for [our new business intelligence team]?"

Data pipelines and infrastructure

1. "What is a data pipeline, and why is it important for [business decision making and forecasting]?"
2. "What is the difference between [ETL and ELT] data workflows?"
3. "Why are modern data warehouses and lakehouses (for example, Snowflake, Databricks) important in [scale-ups]?"
4. "What modern analytics workflows (for example, dbt transformations) are common in [product analytics teams]?"
5. "What cloud data infrastructure skills should recruiters recognize on resumes for a [Senior Data Platform Engineer]?"

AI and machine learning

1. "What is the difference between [Machine Learning Engineers] and [Data Scientists] from a hiring perspective?"
2. "What are LLMs and why are companies building products around them in [specific industry, for example, fintech or e-commerce]?"
3. "How are [AI Engineers] different from traditional [Data Engineers or ML Engineers]?"
4. "What AI and data ecosystem trends should recruiters understand when hiring in [2026]?"
5. "What technical skills (for example, PyTorch, LangChain, vector databases) are commonly expected in [Generative AI application developer] roles?"

Data candidate screening

1. "How can I evaluate a data candidate's [analytical and system modeling depth] without being highly technical?"
2. "What are common red flags when screening [Data Engineer vs Data Scientist] candidates?"
3. "What should I look for when evaluating a data candidate's [portfolio, GitHub repository, Kaggle profile, or Looker dashboard]?"
4. "How do I distinguish between [Junior, Middle, Senior, and Staff] data professionals?"
5. "Create a technical screening scorecard and interview questions for a [Senior Analytics Engineer] role."

Data terminology for HR

1. "Explain [ETL, ELT, data warehouses, machine learning, and vector databases] in simple terms for [new recruiters joining the team]."
2. "What do data teams mean by [scalability, data quality, data lineage, and schema drift]?"
3. "What is the structural difference between [descriptive analytics] and [predictive machine learning]?"
4. "What is a [lakehouse architecture] and why does it matter for [modern data-driven enterprises]?"
5. "Which data terms are [core competencies] versus [transient tools] that I should filter for on resumes?"

Data hiring insights

Junior Data Analyst / Data Engineer

Common expectations:

• Basic SQL knowledge
• Spreadsheet and dashboard familiarity
• Data cleaning basics
• Python or BI tooling awareness
• Basic reporting skills

Mid-level Data Professional

Common expectations:

• Data pipeline familiarity
• Warehousing and analytics workflows
• Dashboard and reporting ownership
• Data modeling awareness
• Collaboration with product and engineering teams

Senior Data Engineer / Data Scientist

Common expectations:

• Scalable data architecture design
• Data quality and governance expertise
• Machine learning or advanced analytics understanding
• Cloud data ecosystem familiarity
• Mentoring and technical leadership
• Cross-functional collaboration

Staff / Lead Data Professional

Common expectations:

• Organization-wide data strategy
• Data platform leadership
• AI and analytics ecosystem planning
• Governance and reliability ownership
• Business alignment and executive communication
• Long-term data infrastructure decisions

Important hiring realities

Data roles are highly specialized

A company may incorrectly expect one person to simultaneously handle:

• data engineering
• dashboards
• machine learning
• AI engineering
• business analytics
• infrastructure
• experimentation
• executive reporting

This is often unrealistic.

SQL alone does NOT equal strong data expertise

A candidate may:

• write SQL queries
• but still lack:
  • data modeling
  • scalability thinking
  • business reasoning
  • analytics maturity
  • production data experience

Modern AI engineering is NOT only prompt engineering

Strong AI Engineers usually understand:

• APIs
• embeddings
• retrieval systems
• evaluation pipelines
• scalability
• data infrastructure
• product integration

rather than only writing prompts.

Strong data professionals often think in systems

Strong candidates usually demonstrate:

• analytical reasoning
• data quality awareness
• scalability thinking
• business understanding
• experimentation mindset
• communication ability
• operational maturity

rather than only tool familiarity.

Common HR misunderstandings

Data Science ≠ Data Engineering

Data Science focuses more on:

• modeling
• experimentation
• forecasting
• statistical analysis

Data Engineering focuses more on:

• infrastructure
• pipelines
• warehousing
• scalability
• data reliability

Dashboards ≠ strong analytics automatically

A candidate may:

• create visually attractive dashboards
• but still lack:
  • metric clarity
  • business reasoning
  • data governance understanding
  • actionable insights

More AI buzzwords ≠ stronger AI candidate

Strong AI and data professionals usually demonstrate:

• systems thinking
• business reasoning
• data quality awareness
• experimentation maturity
• production experience
• scalability understanding

rather than only trending terminology.

Tips

• Senior data professionals should be evaluated on data governance, scalability thinking, and business alignment rather than a laundry list of database tools or visualization frameworks.
• Data portfolios are strongest when they demonstrate end-to-end data reasoning, production pipeline workflows, and concrete business impact, rather than just simple dashboard mockups or isolated notebooks.
• Many companies misuse data titles — recruiters must clarify if the vacancy is engineering-focused (infrastructure/pipelines), analytics-focused (BI/metrics), or science/AI-focused (modeling/algorithms).
• Modern data engineering relies on high-quality SQL, Python, cloud platforms, and analytics engineering (e.g. dbt) as foundational skills, not just secondary tools.
• Avoid unrealistic "unicorn" job descriptions that expect a single individual to master data engineering, advanced machine learning, DevOps, dashboard design, and executive analytics.