Legacy tools, brittle ETL jobs, and siloed teams make it hard to trust or operationalize insights. Galvanize helps enterprises close the gaps by building the engineering capabilities that keep data fresh, accurate, and production-ready.
Our programs combine immersive labs with embedded coaching inside your environment. Teams learn to ingest, transform, and serve data through modern architectures — managing both batch and streaming pipelines using tools like Kafka, Spark, and Airflow. They model data with precision, automate testing and lineage, and deploy machine learning models into production environments.
The results are cleaner pipelines, faster insights, and data systems that evolve as quickly as your products do.
We help engineering and analytics teams build the systems, pipelines, and models that turn raw data into strategic advantage. Programs cover the full lifecycle — from ingestion to production ML — tailored to your stack, systems, and business goals.
Technologies:
PostgreSQL · MySQL · MongoDB · Redis · Snowflake · Databricks · Google BigQuery
We train teams to design and manage scalable, resilient data platforms. Engineers learn schema design, indexing, caching, and partitioning patterns to optimize performance across relational, NoSQL, and cloud-native systems.
Technologies:
Apache Kafka · Apache Spark · Airbyte · Flink · Debezium · AWS Glue
We upskill teams to build high-throughput pipelines that process real-time and batch data. Through hands-on projects, participants learn streaming architectures, event-driven design, and integration patterns that keep data flowing consistently from source to insight.
Technologies:
Pandas · NumPy · Scikit-learn · TensorFlow · PyTorch · Hugging Face · LangChain
We bridge data engineering and applied data science. Teams gain hands-on experience preparing data for analytics, building and evaluating ML models, and deploying them into production environments. We focus on reproducibility, automation, and measurable impact from applied AI.
Technologies:
Airflow · dbt · Prefect · Dagster · ETL/ELT design patterns
We help engineers model, transform, and orchestrate data pipelines with reliability and transparency. Teams master dependency management, versioning, documentation, and testing, building reproducible workflows that scale as data complexity grows.
Technologies:
AWS Glue · Azure Data Factory · Google BigQuery · Terraform · Kubernetes
We train teams to deploy, automate, and operate data systems across cloud platforms. From managing cloud warehouses to provisioning infrastructure as code, programs teach teams how to scale securely and efficiently across AWS, Azure, and Google Cloud.
Technologies:
MLflow · Feature Stores · BentoML · Seldon Core · Kubeflow
We help data teams close the last mile between experimentation and impact. Participants learn to deploy, monitor, and retrain models in production environments, establishing robust MLOps workflows that ensure continuous improvement and compliance.
Technologies:
Great Expectations · Soda · Monte Carlo · OpenLineage · Grafana · Prometheus
We build the discipline of reliable data delivery. Engineers learn to implement testing, monitoring, lineage tracking, and SLAs/SLOs, turning fragile pipelines into dependable data products that support critical business decisions.
Technologies:
GitHub Copilot · Cursor · OpenAI API · Retrieval-Augmented Generation (RAG)
We prepare teams to leverage AI in data workflows, from automated pipeline generation to natural-language data querying and documentation. Engineers learn how to integrate AI tools responsibly to increase productivity, quality, and governance.
For a Fortune 100 company, Galvanize trained multiple cohorts of data scientists and translators across Latin America in Python, machine learning, and AI workflows. Each 12-week program combined instructor-led sessions, pre/post assessments, and capstone projects built with real enterprise data.
Graduates developed predictive models valued at over $114 million, producing a 17× ROI for partner organizations and cementing the company as a flagship case for data-driven transformation in the region.
We analyze your data architecture, pipelines, and team workflows to align skill development with your future-state analytics strategy.
We design role-based curriculum that turns theory into measurable data fluency—linking every module to production use cases, code quality, and analytic accuracy.
We blend instruction with live data projects, enabling teams to design, build, and optimize pipelines while our embedded coaches guide adoption in your environment.
We track impact through pre/post assessments, performance metrics, and production KPIs—proving ROI in speed, reliability, and data quality across teams.
Talk to us about upskilling your teams to cut cycle time, raise data quality, and deliver decisions powered by trustworthy pipelines.