Jobs in Data & AI

Data Analyst

Turn chaotic data into clear insights, build dashboards people actually use, answer “can you pull this real quick?” requests that never take five minutes, and help teams make decisions backed by numbers not gut feelings.

Data Scientist

Build predictive and statistical models, uncover patterns humans miss, communicate complex concepts simply, and spend most of your time preparing messy data so your models can shine during that final demo.

Data Architect

Design modern data ecosystems such as lakes, warehouses, streams and icebergs that scale, ensure data flows cleanly from source to insight, and prevent the entire organization from drifting into a spaghetti mess of pipelines and shadow databases.

Data Engineer / ETL Engineer / DWH Engineer

Build and maintain reliable data pipelines, transform raw data into trusted datasets, design performant warehouses, and ensure downstream teams aren’t trying to make decisions using incomplete or inconsistent data.

Analytics Engineer

Live between data engineering and analytics, own the transformation layer, write maintainable dbt models, and turn raw tables into clean, documented, analyst-ready datasets that don’t break every other day.

BI Analyst

Turn data into business intelligence, build dashboards and visualizations that drive decisions, and explain why numbers differ between systems without blaming “the data” unless it really is the data.

BI Developer

Build enterprise BI solutions, design semantic layers and optimized data models, and create dashboards fast enough that executives stop emailing you Excel screenshots asking for “the latest version.”

AI Engineer

Build and deploy AI solutions, integrate ML models into production systems, and bridge the gap between experimental notebooks and real-world applications that scale reliably and deliver measurable business value.

Machine Learning Engineer

Take ML models from prototype to production, build training and inference pipelines, monitor performance in the wild, and make sure the model that worked on test data doesn’t collapse under real user behavior.

MLOps Engineer

Automate the end-to-end ML lifecycle, manage feature stores, versioning, and model monitoring, detect drift before it becomes a crisis, and make deploying ML models as stable as deploying regular software.

AI Architect

Design enterprise-level AI architectures and strategies, evaluate where AI adds real value, define data/model pipelines, and ensure solutions integrate cleanly instead of becoming unmaintainable collections of experiments.

Prompt Engineer / LLM Engineer

Optimize prompts and model interactions, build evaluation frameworks for LLM performance, fine-tune models for specific use cases, and navigate the constantly evolving world of generative AI where best practices change monthly.

Data Product Manager

Define the roadmap for data products and platforms, translate business needs into data requirements, and balance the tension between delivering quick insights and building scalable foundations for long-term analytics.

Power BI Developer

Build interactive Power BI dashboards, optimize DAX that tests your patience (and CPU), improve report performance, and create visuals that make insights obvious—even for non-technical audiences.

Power BI Analyst

Translate business questions into Power BI reports, connect to every data source imaginable, build DAX measures that reflect business logic, and explain discrepancies between Power BI reports and old Excel files.

Microsoft Fabric Engineer

Build solutions across the Fabric ecosystem, manage lakehouses and pipelines, and orchestrate end-to-end analytics flows in Microsoft’s unified data platform.

Microsoft Fabric Architect

Design enterprise data architectures on Fabric, plan migrations from legacy systems, and architect solutions that use Fabric’s capabilities effectively without locking the organization into long-term complexity.