Data Engineers and
Data Architects
Engineers who design scalable data pipelines, warehouses, and lakehouses using Spark, Databricks, SQL, and Python.
Data, Analytics & AI
Technical talent that can transform the entire organization
The Challenges We Solve
Data engineering, analytics, and AI are among the fastest-moving and most competitive hiring categories in technology. The demand is high, the talent pool is growing but unverified, and the difference between a strong hire and a weak one is often invisible until it is too late. Organizations often rely on general recruiters or platforms that cannot distinguish between candidates with real production experience and those with only academic or proof-of-concept exposure. Meanwhile, organizations building teams need practitioners who build pipelines that hold up in production, data scientists who know the difference between a demo and a deployable model, and AI engineers who have worked in real implementation environments.
Our Approach
At InfoPeople, we bring technical depth to evaluate data and AI candidates rigorously, because our own practitioners work in these disciplines every day. We place data engineers who build pipelines that hold up in production, data scientists who know the difference between a demo and a deployable model, BI professionals who make analytics genuinely useful to decision-makers, and AI engineers who have worked in real implementation environments rather than just academic ones. Because our own Data and AI practice designs production data platforms and agentic AI systems daily, candidates for these roles are screened against the same technical bar our own delivery teams operate at.
Data, Analytics & AI Roles We Place
Data Engineers and
Data Scientists and
ML Engineers
Scientists who build predictive models and machine learning systems, proficient in Python, R, and scikit-learn.
AI Engineers and
AI/ML Practitioners
Engineers with hands-on experience building and deploying AI systems, including LLMs and agentic systems.
Business
Intelligence Developers
Developers who build analytics solutions and dashboards using Power BI, Tableau, Looker, and SQL-based reporting systems.
Data Analysts and
Reporting Analysts
Analysts who translate data into actionable insights, build reports, and support data-driven decision-making across organizations.
Database
Administrators
DBAs who manage database infrastructure including SQL Server, Oracle, PostgreSQL, and cloud-managed database services.
Data Platform
Administrators
Platform-focused administrators who manage data warehouse, lakehouse, and analytics platform infrastructure and governance.
Analytics Leads
and Chief Data Officers
(CDO)
Leadership roles setting data strategy and aligning analytics initiatives with business objectives.
Insights and Resources
Exotic insurance platform transforms underwriting with AI-native valuation and risk intelligence
A unified AI-native valuation infrastructure enabled an exotic insurance provider to reduce underwriting latency, improve pricing precision, and strengthen capital resilience across exotic risk categories.
Global digital publisher strengthens revenue predictability with AI-powered RFP and forecasting engine
A unified AI-backed revenue intelligence platform helped a global publisher reduce forecast variance, improve RFP win rates, and align inventory allocation with probabilistic demand modeling.
FAQs
Candidates are evaluated by practitioners who build AI and agentic systems in production daily, which allows for a real assessment of implementation experience rather than academic or demo-level exposure.
Both. Our Data, Analytics & AI practice covers the full range from data engineers and AI/ML practitioners to business intelligence developers, data analysts, and analytics leadership roles such as Chief Data Officer.
Leverage proven talent with production experience
Get data engineers, data scientists, and AI engineers evaluated by
our own practitioners, not generalist recruiters.