Build AI-ready data pipelines, integrate Azure AI services and OpenAI APIs, operationalize machine learning models with MLOps, and ensure data infrastructure scales reliably.
Data Engineers build and maintain the infrastructure and pipelines that move, transform, and make accessible the data that powers analytics and AI systems.
Data Engineering is one of the fastest-growing specializations in tech. As AI adoption accelerates, companies need engineers who can build production-grade data and AI infrastructure — not just proof-of-concept notebooks.
Modern data engineers increasingly work at the intersection of data, AI, and cloud infrastructure DevOps practices (MLOps).
Start with programming and cloud fundamentals, build through AI services and MLOps, and complete with infrastructure and monitoring skills.
Python is the universal language of data engineering. Data manipulation, API clients, Azure SDK, pipeline scripting, and test harnesses for data pipelines.
Data pipelines run on Linux servers. Shell scripting, cron scheduling, file system management, and process management for data workloads.
Azure subscriptions, resource management, Blob Storage, Azure SQL, Cosmos DB, and Data Factory — the Azure data estate that most pipelines consume.
Integrate Azure Cognitive Services into data pipelines: Vision, Speech, Language, and Form Recognizer APIs for enriching data with AI-extracted features.
Build GPT-powered data applications: embeddings for semantic search, RAG pipelines, prompt design for data extraction, and production OpenAI API integration patterns.
Take ML models from notebook to production: Azure ML pipelines, model versioning, experiment tracking, automated retraining, model monitoring, and CI/CD for ML.
Apply AI to data operations: anomaly detection in pipeline runs, AI-powered data quality checks, intelligent alerting, and automated incident response for data platform issues.
Package data pipeline code as containers: consistent execution environments, dependency isolation, and containerized data jobs that run on Kubernetes or Azure Container Instances.
Provision data infrastructure with Terraform: Azure Data Factory, Storage Accounts, Azure ML workspaces, Key Vault for secrets, and network-secured data estates.
Monitor data pipeline health: custom metrics for pipeline run time, failure rate, data volume, and SLA adherence — visualized in Grafana for the data team and stakeholders.
Build a Python pipeline that ingests PDFs via Azure Blob Storage, extracts structured data with Azure Form Recognizer, enriches with OpenAI embeddings for semantic search, and stores results in Cosmos DB.
Take a trained classification model through Azure ML: register the model, build a CI/CD pipeline for retraining on new data, deploy to an endpoint, and monitor prediction drift vs baseline in Grafana.
Provision an end-to-end Azure data platform with Terraform: Data Lake, Data Factory, Databricks, Key Vault for secrets, and private networking — repeatable across dev, staging, and production.