Career Roadmap
AWS Data Engineer Roadmap 2026 Roadmap : Zero to Hero Guide
This roadmap guides you from data fundamentals to professional data engineering on AWS. You will design data ingestion pipelines, transform data at scale, manage data stores, orchestrate complex workflows, and secure data platforms. SAA-C03 builds the operational foundation. DEA-C01 validates AWS data engineering expertise. MLA-C02 adds ML and GenAI data pipeline specialization. Use ExamOS practice quizzes to track progress at every stage.
Who is this roadmap for?
This roadmap is designed for Data Analysts and BI Developers transitioning into data engineering, as well as Software Engineers moving into data who want to build ETL and pipeline skills. Additionally, it is tailored for Cloud Engineers who want to add data engineering depth, as well as Data Scientists who need to operationalize their models with production data pipelines.
Skills You'll Develop
Write ETL logic, transform data with pandas and PySpark, and interact with AWS services via boto3.
Write complex SQL queries, optimize Athena performance, and tune Redshift query execution.
Build streaming and batch pipelines with Kinesis, Glue, and EMR using medallion architecture.
Design and manage S3 data lakes, Redshift, Athena, and OpenSearch for analytics workloads.
Orchestrate pipelines with Step Functions and MWAA; implement security with Lake Formation and KMS.
Build feature stores and model training pipelines with SageMaker and Glue.
Target Roles in this Roadmap
- AWS Data Engineer: Designs and implements data pipelines and analytics platforms on AWS
- Data Integration Engineer: Builds ETL/ELT pipelines with Glue, Kinesis, and DataSync
- Analytics Engineer: Builds and optimizes data models for analytics and BI
- ML Data Engineer: Builds data pipelines for machine learning and GenAI workloads with SageMaker and Bedrock
- Data Platform Engineer: Manages data lake infrastructure, governance, and security
Typical Employer Categories
- Technology & Software Firms: SaaS platforms, enterprise software vendors, AWS partners
- Financial Services: Banking, fintech, insurance, and payment processors with large data volumes
- Healthcare Organizations: Patient data platforms, clinical analytics, and health informatics
- Retail & E-Commerce: Customer analytics, supply chain optimization, and real-time inventory
- Consulting & Professional Services: Data engineering assessments, AWS transformations, and managed analytics
- AWS Partners: AWS Data & Analytics Competency partners, MSPs, and system integrators
The Certification Path
Recommended Path
| Cert | When | Why |
|---|---|---|
| AWS SAA-C03 (Solutions Architect Associate) | Month 2-4 | AWS operational foundation. Builds the architecture knowledge DEA-C01 scenarios assume. |
| AWS DEA-C01 (Data Engineer Associate) | Month 4-10 | The core AWS data engineering credential. Validates Glue, Kinesis, Redshift, and Lake Formation expertise. |
Optional Foundation
| Cert | When | Why |
|---|---|---|
| AWS CLF-C02 (Cloud Practitioner) | Month 1 | Optional warm-up. Only needed if you are completely new to AWS. |
Specialization Path
| Cert | When | Why |
|---|---|---|
| AWS MLA-C02 (Machine Learning Engineer) | Month 10-14 | ML and GenAI engineering depth. Natural follow-on for data engineers building ML data pipelines. |
| AWS AIP-C01 (GenAI Developer Professional) | Month 10-14 | Generative AI depth. For data engineers building RAG and vector search infrastructure. |
| AWS SAP-C02 (Solutions Architect Professional) | Month 12-16 | Senior architecture depth. For data engineers moving toward architecture roles. |
Milestones: Junior → Mid → Senior
| Level | Milestone | When |
|---|---|---|
| Entry Level | SAA-C03 + basic data pipeline understanding | Month 4-5 |
| Practitioner Level | DEA-C01 + production pipeline proficiency | Month 8-10 |
| Specialist Level | DEA-C01 + MLA-C02 + enterprise data engineering skills | Month 14+ |
Step 1 - Data engineering and AWS foundations
Build the programming, data, and AWS architecture foundation that every data engineering task depends on. This step combines data fundamentals with SAA-C03-level AWS knowledge.
~3-4 months~3-4 months
Step 1 - Data engineering and AWS foundations
Build the programming, data, and AWS architecture foundation that every data engineering task depends on. This step combines data fundamentals with SAA-C03-level AWS knowledge.
- Python proficiency: pandas, PySpark basics, boto3 for AWS API interaction
- SQL fluency: SELECT, JOIN, window functions, CTEs, query optimization
- Data formats: CSV, JSON, Parquet, ORC, Avro. Characteristics and use cases.
- Data engineering concepts: batch vs streaming, ETL vs ELT, medallion architecture
- AWS S3: bucket architecture, storage classes, lifecycle policies, event notifications
- AWS IAM: roles for data services, least-privilege policy design, cross-account access
- AWS networking: VPCs, VPC endpoints for S3, Glue, Redshift, Athena
- AWS compute: EC2, Lambda for pipeline triggers
- AWS CloudWatch: logs, metrics, alarms for pipeline monitoring
Certifications
💡 SAA-C03 is the recommended prerequisite for DEA-C01. Candidates without SAA-C03-level AWS knowledge struggle with the data store and security domains.
💡 CLF-C02 is optional. If you are completely new to AWS, take it as a warm-up. If you already have any AWS experience, skip it and go straight to SAA-C03.
🏁 Entry Level Checkpoint: You have passed SAA-C03. You can reason through AWS networking, IAM, and data service scenarios.
🛠 Project Ideas
- ▸Deploy a VPC with private subnets and VPC endpoints for S3, Glue, and Redshift.
- ▸Write an IAM policy that grants least-privilege access for a Glue job to read from S3 and write to Redshift.
- ▸Write a Python script that reads a CSV, transforms it with pandas, and writes it to Parquet in S3.
Step 2 - AWS data engineering (DEA-C01)
Build professional data engineering skills on AWS. DEA-C01 covers Data Ingestion and Transformation (34%), Data Store Management (26%), Data Operations and Support (22%), and Data Security and Governance (18%).
~4-6 months~4-6 months
Step 2 - AWS data engineering (DEA-C01)
Build professional data engineering skills on AWS. DEA-C01 covers Data Ingestion and Transformation (34%), Data Store Management (26%), Data Operations and Support (22%), and Data Security and Governance (18%).
- Data Ingestion (34%): Kinesis Data Streams vs Firehose, MSK, DMS (CDC), DataSync, Snow family
- Data Transformation (34%): AWS Glue (Data Catalog, crawlers, ETL jobs, job bookmarks, worker types), Glue Data Quality, Glue DataBrew, EMR
- Data Store Management (26%): S3 data lake (partitioning, compaction), Redshift (distribution styles, sort keys, Spectrum), Athena (partition pruning, file optimization), OpenSearch
- Data Operations (22%): Step Functions, MWAA, Glue Workflows, EventBridge, performance tuning, troubleshooting
- Data Security (18%): Lake Formation (fine-grained access control), KMS, CloudTrail data events, Macie, VPC endpoints
Certifications
💡 DEA-C01 is the most technically demanding associate-level AWS certification. The 34% weight of Data Ingestion and Transformation, primarily Glue and Kinesis, means surface-level knowledge will not pass.
💡 Glue job bookmarks and worker type selection appear in incremental processing and cost optimization scenarios.
💡 Lake Formation permissions sit on top of IAM. Both need to allow an action for it to succeed.
🏁 Practitioner Level Checkpoint: You have passed DEA-C01. You can design, implement, and operate production data pipelines on AWS.
🛠 Project Ideas
- ▸Build a complete medallion architecture: ingest raw data with Kinesis, transform with Glue (Bronze to Silver to Gold), and query with Athena.
- ▸Build a Redshift cluster with KEY distribution and sort keys. Optimize query performance using EXPLAIN and WLM.
- ▸Configure Lake Formation with column-level security. Grant access to different user groups.
- ▸Build a Step Functions state machine that orchestrates multiple Glue jobs with parallel processing.
Step 3 - ML and GenAI data engineering specialization (MLA-C02)
Extend your data engineering skills to machine learning and generative AI workloads. MLA-C02 replaces MLA-C01 and validates the ability to build ML data pipelines, feature stores, model training infrastructure, and GenAI solutions on AWS.
~3-4 months~3-4 months
Step 3 - ML and GenAI data engineering specialization (MLA-C02)
Extend your data engineering skills to machine learning and generative AI workloads. MLA-C02 replaces MLA-C01 and validates the ability to build ML data pipelines, feature stores, model training infrastructure, and GenAI solutions on AWS.
- Data Preparation for ML and AI (28%): data collection, transformation, feature engineering, bias detection, vector database configuration
- ML Model and Foundation Model Development (24%): model selection, training, fine-tuning, hyperparameter tuning, RAG optimization
- Deployment and Orchestration (24%): deployment infrastructure, auto scaling, CI/CD pipelines for MLOps and AI workloads
- Operating, Monitoring, and Securing (24%): model monitoring, drift detection, cost optimization, AI security controls
- SageMaker Feature Store: feature groups, online vs offline stores, ingestion patterns
- SageMaker Pipelines: automated model training pipelines, integration with Glue and Lambda
- Amazon Bedrock: foundation model selection, fine-tuning, RAG architectures, agentic workflows
💡 MLA-C01 retired in English on September 28, 2026. MLA-C02 is the current exam. The beta version launched September 29, 2026, with 85 questions and a 170-minute time limit. General availability is expected in early 2027.
💡 MLA-C02 adds generative AI implementation, fine-tuning foundation models, RAG architectures, and agentic AI to the traditional ML engineering content.
💡 AIP-C01 (Generative AI Developer Professional) is an alternative specialization for data engineers building RAG and vector search infrastructure.
🏁 Specialist Level Checkpoint 1: You have passed DEA-C01 and MLA-C02. You can design, implement, and operate enterprise-grade data, ML, and GenAI pipelines on AWS.
🛠 Project Ideas
- ▸Build a complete ML pipeline: ingest training data with Glue, create a Feature Store, train a model with SageMaker Pipelines, register it in Model Registry, and deploy to an endpoint.
- ▸Build a RAG pipeline with Bedrock Knowledge Bases and OpenSearch vector search.
- ▸Configure model monitoring to detect data drift and quality drift in production.
- ▸Implement an agentic workflow that orchestrates multiple AI agents for a complex task.
Step 4 - Exam consolidation and career advancement
Consolidate your skills, validate with your target certifications, and plan your next career move. The certification is the milestone. The career is the journey.
~1 month~1 month
Step 4 - Exam consolidation and career advancement
Consolidate your skills, validate with your target certifications, and plan your next career move. The certification is the milestone. The career is the journey.
- Full scenario practice: end-to-end data and ML pipelines from ingestion to inference
- Domain-weighted gap analysis: identify weak areas across DEA-C01 domains
- Exam readiness: consistent 80%+ on Legend mode across five sessions
- Follow-on paths: AIP-C01 (GenAI) for RAG and vector search, SAP-C02 for architecture depth
Certifications
💡 Consistent performance above 80% on Legend mode across five consecutive ExamOS sessions is the clearest readiness signal for all certifications.
💡 AIP-C01 is the natural follow-on for data engineers building generative AI infrastructure with RAG and vector search.
💡 SAP-C02 is the natural follow-on for data engineers moving toward architecture roles.
🏁 Specialist Level Checkpoint 2: You have passed your target certifications. You can design and operate enterprise-grade data, ML, and AI solutions on AWS.
🛠 Project Ideas
- ▸Build a complete end-to-end platform: data ingestion with Kinesis, transformation with Glue, data storage in Redshift and S3, orchestration with Step Functions, security with Lake Formation, and ML pipelines with SageMaker.
- ▸For AIP-C01 specialization: build a RAG pipeline with Bedrock Knowledge Bases and vector search.
Final Step - What this path builds
This roadmap builds a professional data engineer who can design data ingestion pipelines, transform data at scale, manage data stores, orchestrate complex workflows, and secure data platforms. DEA-C01 is the most technically demanding associate-level AWS certification. The 34% weight of Data Ingestion and Transformation, primarily Glue and Kinesis, means surface-level knowledge will not pass. The most common failure pattern is knowing services individually without reasoning through multi-service pipeline architectures. After DEA-C01, MLA-C02 is the natural follow-on for ML and GenAI data pipelines, and AIP-C01 is the natural follow-on for generative AI data infrastructure. Build real pipelines. Measure your readiness with ExamOS. Book when your reasoning is sharp and your architectures are correct.
Final Step - What this path builds
This roadmap builds a professional data engineer who can design data ingestion pipelines, transform data at scale, manage data stores, orchestrate complex workflows, and secure data platforms. DEA-C01 is the most technically demanding associate-level AWS certification. The 34% weight of Data Ingestion and Transformation, primarily Glue and Kinesis, means surface-level knowledge will not pass. The most common failure pattern is knowing services individually without reasoning through multi-service pipeline architectures. After DEA-C01, MLA-C02 is the natural follow-on for ML and GenAI data pipelines, and AIP-C01 is the natural follow-on for generative AI data infrastructure. Build real pipelines. Measure your readiness with ExamOS. Book when your reasoning is sharp and your architectures are correct.
Certifications
Final Thoughts
💡 Total: 7-12 months at 2 hours/day (5-8 months at 3-4 hours/day)
💡 SAA-C03: 2-3 months. AWS operational foundation.
💡 DEA-C01: 4-6 months. Core data engineering credential.
💡 MLA-C02: 3-4 months. ML and GenAI data pipeline specialization.
💡 Readiness: The ExamOS Readiness Score updates after every practice session and recommends when to schedule the exam.
Honest Timeline
| Path | Minimum Study Pace | More Realistic Pace |
|---|---|---|
| CLF-C02 only (optional) | ~2 weeks | ~1 month |
| SAA-C03 only | ~2 months | ~3 months |
| SAA-C03 + DEA-C01 | ~5 months | ~8 months |
| SAA-C03 + DEA-C01 + MLA-C02 | ~7 months | ~12 months |