Data Engineers

AI Developers to Data Engineers: The Talent HR Teams Need Most in 2025

Indonesia's demand for digital talent keeps climbing. The Ministry of Communication and Informatics projects the country will need 9 million digital professionals by 2030. At the IDCamp 2025 press conference, Indosat Ooredoo Hutchison's CHRO, Irsyad Sahroni, and Dicoding's CEO, Narenda Wicaksono, made it clear: AI developers, machine learning specialists, data analysts, and data engineers are the priority hires.

They also underscored a blunt truth for HR leaders: AI is already inside HR. It can spot attrition risks, surface promotion potential, and highlight what keeps people engaged. But most companies still struggle to make AI useful because their data is scattered and messy-hence the surge in demand for strong data engineers.

Ministry of Communication and Informatics

What this means for HR

• Budget and headcount should shift toward data and AI roles. Without clean, structured data, AI projects stall.
• Hiring now favors hybrid talent: technical depth plus business context and adaptability.
• HR systems need to produce reliable data pipelines. Your ATS, HRIS, and L&D platforms must feed analytics-not just store records.

Roles to prioritize (and what to look for)

• Data Engineer: Builds pipelines, cleans and models data, ensures quality and reliability. Look for SQL, Python, ETL/ELT, data warehousing (BigQuery/Redshift/Snowflake), and orchestration (Airflow/DBT).
• Machine Learning Specialist/Engineer: Trains, evaluates, and deploys models. Look for Python, scikit-learn/TF/PyTorch, MLOps, and model monitoring.
• AI Developer: Ships AI-enabled apps and integrations. Look for API design, prompt skills, RAG, vector databases, and evaluation of model outputs.
• Data Analyst: Turns raw data into decisions. Look for SQL, dashboards (Looker/Power BI/Tableau), and stakeholder storytelling.

Practical plays HR can run this quarter

• Update job architectures: Add clear levels for Data Engineer, ML Engineer, AI Developer, and Data Analyst with competencies, interview rubrics, and pay bands.
• Redesign hiring loops: Pair a take-home data exercise with a live problem-solving session tied to your business (e.g., churn reduction, sales forecasting, workforce planning).
• Fix your data sources: Ask IT to centralize HR, finance, CRM, and product data into one warehouse. Make data quality a KPI.
• Start with one AI use case in HR: Attrition risk scoring or promotion-readiness signals. Keep a human in the loop for final decisions.
• Stand up basic governance: Define data owners, retention policies, bias checks, and model review cadence. Document everything.

How to assess candidates without guesswork

• Portfolio over pedigree: Ask for repos, notebooks, dashboards, and shipped features.
• Business context test: Give a messy dataset and a clear outcome (e.g., "Cut support tickets by 10%"). Judge clarity of assumptions and impact, not just code.
• Systems thinking: Probe how they monitor pipelines/models, handle data drift, and prevent failure cascades.
• Communication: Can they explain trade-offs to non-technical leaders in one slide?

Build the data foundation before you scale AI

• Single source of truth: Centralize core data (customers, products, employees) in a warehouse. Enforce schema and naming standards.
• Lineage and documentation: Use a data catalog so teams know where data comes from and how it's used.
• Access controls: Role-based permissions. Sensitive HR data needs strict audits and encryption.
• Monitoring: Track data freshness, completeness, and model outcomes. Alert on anomalies.

AI in HR: high-value use cases

• Attrition prediction: Flag at-risk employees so managers can intervene with fair workload, recognition, or growth paths.
• Promotion signals: Combine skills, performance, and learning data to surface internal candidates.
• Workforce planning: Forecast role demand and skill gaps by business unit and quarter.

Keep models explainable, audited, and bias-tested. Use AI as decision support-final calls stay with people.

Upskilling paths for your org

• For HR teams: Data literacy, prompt skills, fundamentals of AI ethics, and vendor evaluation.
• For analysts: SQL depth, dbt, dashboard standards, experiment design, and stakeholder storytelling.
• For engineers: ETL/ELT at scale, orchestration, MLOps, monitoring, and cost control.

Need a curated starting point? Explore job-based learning tracks and certifications here: Complete AI Training - Courses by Job.

What the experts said

Irsyad Sahroni highlighted that AI developers, machine learning specialists, data analysts, and data engineers are the most needed roles right now-and that people who pair skills with adaptability will stand out.

Narenda Wicaksono pointed to the biggest blocker: messy, scattered data. The companies that win are the ones that can organize, extract, and process data so AI can inform decisions.

Bottom line for HR

• Prioritize data engineers and ML/AI roles in your workforce plan and budget.
• Stand up a clean data stack before scaling AI projects.
• Hire for business sense and adaptability, not just technical depth.
• Use AI to support HR decisions, with clear guardrails and human oversight.