Learning Path: Data Engineer (AI Pipelines)

TL;DR

Data Engineers build and maintain the infrastructure that collects, stores, transforms, and serves data to AI and ML systems. The AI Pipelines specialisation means focusing on reliable, scalable pipelines that feed training data to models and serve inference-time data to production AI applications. Without good data engineering, ML models cannot be trained or serve real users.

Last verified 1 April 2026

Why this matters right now

AI systems are only as good as the data they are trained on and the pipelines that serve them. Employment demand is strong and growing. Gartner predicts 75% of organisations will deploy AI/ML integrated with data engineering processes by 2025. Salaries range from $110,000–$160,000 in the US, with senior roles and cloud specialists commanding significantly more.

How this technology has evolved

Beginner (0–4 months): SQL (the single most important skill — master queries, joins, window functions, aggregations), Python for scripting pipelines, data modelling basics (dimensional modelling, star schema), understanding ETL vs. ELT, basic cloud storage (S3, GCS, Azure Blob), Git. Intermediate (4–10 months): Apache Spark/PySpark for distributed processing, Apache Airflow or Prefect for orchestration, data warehousing (Snowflake, BigQuery, or Databricks), dbt for data transformation, streaming with Apache Kafka, data quality and testing, Docker, and deep knowledge of one cloud platform (AWS, GCP, or Azure). Advanced (10–18 months): Real-time AI feature pipelines, building training data pipelines (large-scale data curation and deduplication for LLM training), vector database management and embedding pipelines, MLOps integration, data lakehouse architecture (Delta Lake, Apache Iceberg), Infrastructure as Code (Terraform), and data governance (lineage, cataloging).

Recommended course

Recommended starting point

This course serves as an entry point for professionals looking to transition into data engineering and AI development by establishing a baseline understanding of how algorithms interpret information. Upon completion, learners will grasp the fundamental concepts of model training, data preprocessing, and the basic mechanics behind machine learning workflows. It does not provide instruction on the complex cloud infrastructure or distributed computing architectures required for production-level AI pipelines. Starting here is essential, as mastering these core principles is a prerequisite for building the reliable data systems that Gartner identifies as a critical organizational requirement for the coming years.

Course	Machine Learning for Absolute Beginners
Provider	Prov alison
Level	Beginner
Cost	Free to learn, optional paid certificate

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What this means for your roadmap

Core tools: SQL (primary), Python (PySpark, scripting). Processing: Apache Spark/PySpark, Apache Flink. Orchestration: Apache Airflow, Prefect, Dagster. Transformation: dbt (data build tool). Streaming: Apache Kafka, AWS Kinesis. Warehousing: Snowflake, BigQuery, Databricks, Redshift. Storage formats: Parquet, Delta Lake, Apache Iceberg. Feature stores: Feast, Hopsworks, Tecton. Cloud: AWS (S3, Glue, EMR), GCP (BigQuery, Dataflow), Azure Data Factory. IaC: Terraform. Recommended certifications: dbt Certified Developer, Databricks Certified Associate Developer for Apache Spark, Google Professional Data Engineer, AWS Data Analytics Specialty.