Engineer scalable data pipelines on Snowflake and turn diverse data sources into trusted, analytics-ready information.

Snowflake Data Engineer develops practical skills across the modern data engineering workflow, from ingesting and transforming structured, semi-structured, and unstructured data to orchestrating pipelines and optimizing performance.

Work with Snowpipe, Snowpipe Streaming, Snowpark, Streams, Tasks, Dynamic Tables, Cortex LLM functions, observability, and cost controls while applying Snowflake-recommended practices.

  • Why get trained: Develop practical skills across data ingestion, transformation, orchestration, performance optimization, delivery, and observability
  • Why it matters: Reliable data engineering ensures data can move efficiently from source systems into trusted, scalable pipelines that support analytics and AI workloads
  • Who should attend: Data engineers, data analysts, data scientists, database architects, database administrators, and data application developers

Apply Snowflake’s data engineering capabilities to create efficient pipelines, optimize workloads, and deliver dependable data for analytics and AI use cases. HRD Corp Claimable.

Overview

This three-day role-specific course covers key concepts, features, considerations, and Snowflake recommended best practices through the lens of the data engineering workflow.

It is intended for participants who will be accessing, developing, and querying datasets for analytic tasks and building data pipelines in Snowflake.

This course consists of core data engineering concepts delivered through lectures, demos, labs, and discussions.

Skills Covered

  • Describe the data engineering workflow and how the Snowflake AI Data Cloud features support the various components of the workflow.
  • Access Snowflake through the Snowsight UI and by using application methods.
  • Load and unload data sets.
  • Configure Snowflake features to cover a range of data ingestion and processing latencies.
  • Develop applications for Snowflake, including comprehensive ANSI standard SQL support.
  • Employ performance and cost optimization techniques.
  • Use Snowflake’s capabilities to work effectively with structured, semi-structured, and unstructured data in Snowflake.
  • Tune queries and improve performance using advanced techniques such as data clustering and materialized views.
  • Employ Snowflake SQL extensibility features such as user-defined functions and stored procedures.

Prerequisites

  • Recommended completion of the “Snowflake Multi-Factor Authentication (MFA) Essentials” free on-demand course.
  • Completion of “Snowflake Foundations” one-day course or equivalent Snowflake knowledge.
  • A background in data engineering is required.

Target Audience

  • Data Analysts
  • Data Engineers
  • Data Scientists
  • Database Architects
  • Database Administrators
  • Data Application Developers

Course Curriculum

Module 1: Snowflake AI Data Cloud

Module 2: Introduction to the Data Engineering Workflow

Module 3: Supporting Platform Features

  • Authentication Methods
  • Drivers, Clients, and Connectors Overview
  • Role-based Access Control (RBAC) Overview
  • Introduction to Data Governance
  • QueryTags

Module 4: Data Storage

  • Semi-structured Data
  • QuerySemi-structured Data
  • Data Lake
  • Apache Iceberg™ Tables
  • External Tables

Module 5: Ingestion

  • Bulk vs. Continuous Data Loading Approaches
  • Snowpipe
  • Snowpipe Streaming
  • Snowflake Connector for Kafka
  • Snowflake Connector for Kafka with Snowpipe Streaming
  • Snowflake Data Loading Best Practices
  • Loading Semi-structured Data
  • SchemaDetection
  • Working with Unstructured Data

Module 6: Transformation

  • Extensibility Overview
  • Snowflake Scripting
  • UDFsandUDTFs
  • Extend Snowflake with Java and Python
  • External Functions
  • External Network Access
  • Data Sharing
  • Introduction to Snowpark
  • Working with Snowflake Notebooks
  • Transformations with Unstructured Data

Module 7: Powering Data with Snowflake LLMs

  • Document AI
  • Cortex LLM Functions Overview
  • Cortex LLM Task-specific Functions
  • Cortex LLM Complete Functions
  • Cost Monitoring

Module 8: Orchestration

  • Creating and Managing Tasks
  • Creating and Managing Streams
  • Streams on Views
  • Using Streams and Tasks Together
  • Dynamic Tables

Module 9: Performance Optimization

  • Natural Clustering
  • Explicit Clustering
  • Automatic Clustering Service
  • Search Optimization Service
  • SQLPerformance Tips
  • Performance Bottleneck Scenarios

Module 10: Delivery

  • Materialized Views
  • Unloading Semi-structured Data
  • Secure Views

Module 11: Management and Observability

  • Observability on Snowflake
  • Outbound Notifications
  • Snowflake Alerts
  • Data Metric Functions
  • System DMF
  • CustomDMF
  • Observability Within Snowsight
  • Cost Controls
  • Resource Monitors

Dates & Locations

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November 24, 2026 - November 26, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 1688
RM 13500
Trainocate exam and cert

Exam & Certification

The SnowPro® Advanced: Data Engineer Certification DEA-C02.

SNOWPRO ADVANCED: DATA ENGINEER OVERVIEW

This certification will test the ability to:

  • Source data from Data Lakes, APIs, and on-premises
  • Transform, replicate, and share data across cloud platforms
  • Design end-to-end near real-time streams
  • Design scalable compute solutions for Data Engineer workloads
  • Evaluate performance metrics

Training & Certification Guide

Exam Version: DEA-C02
Total Number of Questions: 65
Question Types: Multiple Select, Multiple Choice, Interactive
Time Limit: 115 minutes
Language: English
Registration fee: $375 USD
India Registration fee: $300 USD
Passing Score: 750 + Scaled Scoring from 0 – 1000
Unscored Content: Exams may include unscored items to gather statistical information for future use. These items are not identified on the form and do not impact your score, and additional time is factored into account for this content.

Prerequisites: SnowPro Core Certified
Delivery Options:

  • 1: Online Proctoring
  • 2: Onsite Testing Centers

EXAM DOMAIN BREAKDOWN

The table below lists the main content domains and their weightings.

Domain Domain Weightings
1.0 Data Movement 26%
2.0 Performance Optimization 21%
3.0 Storage and Data Protection 14%
4.0 Data Governance 14%
5.0 Data Transformation 25%

RECOMMENDED TRAINING

As preparation for this exam, we recommend a combination of hands-on experience, instructor-led training, and the utilization of self-study assets.

Instructor-Led Course recommended for this exam:

Snowflake Data Engineer Training

Register for the Snowflake Practice Exam now:

SnowPro Practice Exam: Data Engineer

Frequently Asked Questions

ou will learn how to build, transform, orchestrate, optimize, and monitor data pipelines using the Snowflake AI Data Cloud.

The course follows the end-to-end data engineering workflow, helping you develop practical skills across:

  • Structured, semi-structured, and unstructured data
  • Apache Iceberg and external tables
  • Batch and continuous data ingestion
  • Snowpipe and Snowpipe Streaming
  • Kafka integration
  • Snowflake Scripting and extensibility
  • Snowpark and Snowflake Notebooks
  • Tasks, Streams, and Dynamic Tables
  • Performance and cost optimization
  • Observability and data quality monitoring

You will also explore how Snowflake’s AI capabilities, including Document AI and Cortex LLM functions, can be incorporated into modern data workloads.

Pro Tip: Focus on how ingestion, transformation, orchestration, and observability connect into one pipeline. Understanding the complete workflow is more valuable than learning individual Snowflake features in isolation.

This course is ideal if you already have a data engineering background and want to build or manage data pipelines and analytics workloads using Snowflake.

The course is suitable for:

  • Data Engineers
  • Data Analysts
  • Data Scientists
  • Database Architects
  • Database Administrators
  • Data Application Developers

Although the page categorizes the course as fundamental-level Snowflake training, it assumes that you already understand data engineering concepts. The emphasis is therefore on applying those concepts within Snowflake rather than learning data engineering from scratch.

Pro Tip: If you work with ETL/ELT pipelines, data warehouses, SQL, or cloud data platforms but are relatively new to Snowflake, this course provides a structured way to transfer those existing skills into the Snowflake ecosystem.

You should already have a background in data engineering and foundational Snowflake knowledge before attending.

We recommend completing:

  • Snowflake Foundations, or having equivalent Snowflake knowledge
  • Snowflake Multi-Factor Authentication (MFA) Essentials, available as free on-demand training

A data engineering background is also required.

This foundation is important because the course moves quickly into topics such as continuous ingestion, semi-structured data, Snowflake Scripting, Snowpark, orchestration, clustering, materialized views, observability, and cost management.

Pro Tip: Refresh your SQL, data pipeline, ETL/ELT, and data warehouse concepts before attending. This will allow you to concentrate on how Snowflake implements these capabilities rather than relearning the underlying concepts.

es. The course covers multiple ingestion and orchestration patterns for different data latency requirements.

You will learn how Snowflake supports both bulk and continuous data loading, including:

  • Bulk data loading
  • Snowpipe
  • Snowpipe Streaming
  • Snowflake Connector for Kafka
  • Kafka with Snowpipe Streaming
  • Tasks
  • Streams
  • Streams on Views
  • Dynamic Tables

The course specifically aims to help you configure Snowflake features for a range of data ingestion and processing latencies, making these skills applicable to both traditional batch pipelines and near-real-time data engineering scenarios.

Pro Tip: Pay attention to when each ingestion pattern should be used, not just how to configure it. Choosing between batch, continuous, and streaming approaches should be driven by latency, cost, complexity, and business requirements.

Yes. The current curriculum extends beyond traditional data pipelines into unstructured data processing and Snowflake’s AI capabilities.

You will work with unstructured data during storage, ingestion, and transformation and explore Snowflake capabilities including:

  • Document AI
  • Cortex LLM functions
  • Task-specific Cortex LLM functions
  • Cortex LLM Complete functions
  • Snowflake Notebooks
  • Snowpark
  • LLM cost monitoring

This makes the course relevant to data engineers whose responsibilities are expanding beyond traditional structured analytics into the data foundations that support AI applications and workloads.

Pro Tip: Do not treat the AI module as separate from data engineering. AI applications still depend on reliable ingestion, transformation, governance, observability, and cost control, which are core skills developed throughout this course.

Data Engineer 1 builds your core Snowflake data engineering capabilities, while Data Engineer 2 is the progression for learners ready to tackle more advanced engineering scenarios.

SNOW-DE1 establishes the broader data engineering workflow across storage, ingestion, transformation, orchestration, optimization, delivery, and observability. Trainocate’s Snowflake portfolio then positions SNOW-DE2: Snowflake Data Engineer 2 as the next course in the Data Engineer learning path.

Data Engineer 2 extends the learning into more advanced Snowflake engineering topics and assumes that you already possess the foundational platform and pipeline skills covered at Level 1.

Pro Tip: Start with Data Engineer 1 if you are still building your Snowflake-specific engineering foundation. Progress to Data Engineer 2 once you can confidently work with Snowflake ingestion, transformation, orchestration, and optimization workflows.

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