CertLabProAWS Certified Data Engineer Associate
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Última revisão: April 2026
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The AWS Certified Data Engineer Associate (DEA-C01) launched in March 2024 as the practitioner-focused successor to the retired Data Analytics Specialty. It validates the ability to design, build, operate, and secure data pipelines and analytics workloads on AWS — including ingestion, transformation, storage, orchestration, and governance. The exam targets working data engineers, analytics engineers, and ETL developers on AWS-centric stacks. Heavy emphasis on Glue, Lambda, Kinesis Data Streams / Firehose, Managed Kafka (MSK), S3 data lakes, Lake Formation, Athena, Redshift, and EMR. Expect scenario-driven questions about cost-aware ingestion choices, file format and partitioning strategy, and pipeline reliability. DEA-C01 is conceptual (no labs) but assumes hands-on pipeline experience.
The largest domain at 34%. Kinesis Data Streams vs. Firehose vs. MSK selection, Glue ETL jobs and DataBrew, Lambda for lightweight ETL, and AppFlow for SaaS sources. Common stumbling block: choosing the right ingestion service under latency and ordering constraints.
S3 data lake design, file formats (Parquet, ORC, Avro), partitioning, Lake Formation governance, Redshift architecture (RA3, Serverless), and DynamoDB for operational workloads. Tests practical storage tradeoffs.
Workflow orchestration with Step Functions, Glue Workflows, MWAA (Managed Airflow), and EventBridge. CloudWatch monitoring of data jobs, retries, and alerting. Often missed: when MWAA is justified vs. simpler Step Functions.
Lake Formation permissions, fine-grained access via row/column-level security, KMS for at-rest encryption, IAM patterns for cross-account data sharing, and PII detection (Macie). Smaller weight (18%) but high-density questions.
$105k–$150k–$215k USD annual
Range covers US-based mid-to-senior data engineering roles where AWS proficiency is required. FAANG and large data-intensive companies frequently exceed $260k TC at senior levels. Entry roles and non-coastal markets trend lower. DEA-C01 is a credible signal but rarely a sole hiring factor.
Source: levels.fyi 2025–2026 data engineering roles, U.S. BLS OEWS May 2024 (15-1252 software developers, 15-2051 data scientists). Figures are approximate; actual compensation depends on role, region, and experience.
Data engineering hiring stayed strong through 2024–2026 as enterprises continued building cloud data lakes, lakehouse architectures, and analytics platforms. DEA-C01 functions as a credible AWS-specific signal alongside Snowflake, Databricks, or dbt experience. Recruiters at AWS-centric data shops use it as a fast filter together with SQL, Python, and Spark fluency. It pairs naturally with the Solutions Architect Associate (SAA-C03), the Machine Learning Engineer Associate (MLA-C01), and provider-neutral tools like Airflow and dbt. The cert does NOT by itself qualify candidates for staff data-engineer or principal data-platform roles — those expect proven large-scale pipeline ownership and broader system-design experience.
There are no formal prerequisites. AWS recommends at least 2–3 years of general data-engineering experience and at least one year of hands-on AWS data-services experience.
Most candidates approach DEA-C01 after SAA-C03 (architectural foundation) or directly from a strong Spark/SQL/Python background. CLF-C02 is a useful warm-up for career changers without AWS exposure. The most efficient personal-project preparation is an end-to-end pipeline: Kinesis Firehose → S3 (Parquet, partitioned) → Glue catalog → Athena and Redshift Serverless, with Step Functions or Glue Workflows for orchestration and Lake Formation for governance. Candidates from non-AWS data backgrounds (e.g., on-prem Hadoop or pure Snowflake) should plan extra time on Glue, Lake Formation, and the Kinesis family.
DEA-C01 is rated Associate and is comparable in difficulty to SAA-C03, with a more focused service surface. Plan 70–110 hours over 8–12 weeks for candidates with prior data-engineering experience; 120–160 hours for those without. The exam is 65 scored questions in 130 minutes — multiple-choice and multiple-response, no labs.
Common stumbling blocks include differentiating Kinesis Data Streams (custom consumers, ordering, retention) from Firehose (managed delivery, transformations) and MSK (Kafka-compatible); knowing which orchestrator (Step Functions, Glue Workflows, MWAA, EventBridge Scheduler) suits a given pipeline; and Lake Formation permission inheritance edge cases. File-format and partitioning math (compression ratios, Parquet column pruning) shows up regularly.
Initial general availability. Beta exam ran late 2023. Replaces the retired Data Analytics Specialty (DAS-C01) for engineering-focused candidates. Current version as of April 2026.
DEA-C01 (AWS Certified Data Engineer Associate) is a a moderately difficult exam expecting practical hands-on experience plus solid understanding of best practices Associate-level exam. Most candidates need 80–150 hours of study spread over 6–12 weeks for associate-level exams. Most candidates who score consistently above the passing threshold on practice exams pass on their first attempt.