IICTI Certified Data Engineer

Holders demonstrate that they can apply and evaluate lakehouse architecture and open table format internals, stateful stream processing and delivery semantics, change data capture and schema evolution at scale and distributed query engine performance engineering, plus 7 further areas, in realistic professional scenarios, as defined in the AQSCERT scheme for Data Engineer.

Who it is for: Professionals working, or preparing to work, in data roles.

1. 01Lakehouse architecture and open table format internals8% of the exam

   • Architect lakehouse storage layouts and table maintenance strategies that balance concurrency, query performance and cost.
   • Scope: Apache Iceberg, Delta Lake and Hudi metadata and snapshot models, ACID and optimistic concurrency, partition evolution, compaction and snapshot expiry, catalogs (Unity, Polaris, Glue, Nessie) and medallion layering.
2. 02Stateful stream processing and delivery semantics10% of the exam

   • Design streaming pipelines with correct event-time semantics and end-to-end exactly-once guarantees under failure.
   • Scope: Kafka partitioning and transactions, Apache Flink and Spark Structured Streaming, event time, watermarks and late data, windowing, state backends and checkpointing, exactly-once sinks and backpressure.
3. 03Change data capture and schema evolution at scale9% of the exam

   • Design integration pipelines that propagate source changes correctly while tolerating schema evolution and out-of-order events.
   • Scope: Log-based CDC with Debezium, transactional outbox pattern, idempotent merge/upsert, SCD Type 2 from CDC streams, schema registries and Avro/Protobuf compatibility modes, handling of deletes and reordering.
4. 04Distributed query engine performance engineering11% of the exam

   • Diagnose and resolve performance bottlenecks in distributed data workloads using execution plans and runtime metrics.
   • Scope: Spark adaptive query execution, shuffle and skew handling, broadcast and sort-merge joins, file sizing and small-file mitigation, Z-ordering and liquid clustering, Trino and cost-based optimisation, query plan analysis.
5. 05Analytical data modelling for modern platforms10% of the exam

   • Evaluate and select modelling approaches that meet analytical, historisation and change-management requirements.
   • Scope: Kimball dimensional design, Data Vault 2.0, one-big-table and wide-table trade-offs, modelling for columnar lakehouses, dbt project structure, incremental models and semantic-layer readiness.
6. 06Orchestration and pipeline reliability engineering12% of the exam

   • Lead the design of resilient pipeline operations with defined SLOs, safe backfills and fast recovery.
   • Scope: Airflow, Dagster and asset-based orchestration, idempotency and determinism, backfills and reprocessing, retries and dead-letter handling, dependency and freshness SLAs, data pipeline SLOs and on-call practice.
7. 07Data quality engineering and data contracts9% of the exam

   • Design enforceable data quality controls and producer-consumer contracts that stop bad data before it reaches consumers.
   • Scope: Data contracts (Open Data Contract Standard), schema and semantic validation, Great Expectations, Soda and dbt tests, statistical anomaly detection, write-audit-publish patterns and pipeline circuit breakers.
8. 08Data observability, lineage and metadata management7% of the exam

   • Diagnose data incidents and assess downstream impact using lineage, metadata and observability signals.
   • Scope: OpenLineage and Marquez, DataHub and OpenMetadata, column-level lineage, freshness, volume and distribution monitoring, impact analysis, data incident triage and root-cause analysis.
9. 09Data platform security, privacy and governance controls12% of the exam

   • Architect data access and privacy controls that satisfy regulatory obligations without blocking legitimate analytics.
   • Scope: RBAC and ABAC, row filtering and column masking, tokenisation and pseudonymisation, GDPR erasure in immutable table formats, encryption and key management, catalog-based governance and DAMA-DMBOK alignment.
10. 10Data platform strategy, data products and cost governance8% of the exam

    • Evaluate platform architecture and operating-model options and lead decisions on cost, ownership and scalability.
    • Scope: Data mesh and domain ownership, data product design and SLAs, federated computational governance, storage/compute separation, warehouse and lakehouse FinOps, workload isolation and build-versus-buy decisions.
11. 11Data infrastructure for machine learning and AI workloads4% of the exam

    • Design data pipelines that deliver reproducible, governed and fresh data to ML training and AI retrieval systems.
    • Scope: Training dataset versioning (lakeFS, Delta time travel), feature pipelines, unstructured data and document processing, embedding generation and vector index refresh, RAG ingestion pipelines and data provenance.

Question types

Single choice 100%

Delivery

Online (remote proctored), Test centre, On-site (corporate)

Retakes

Up to three attempts within any 12-month period. A 14-day waiting period applies after a first unsuccessful attempt and 30 days after a second. Each retake requires a new booking and fee. Retakes draw a different randomised form from the question bank.

Provisional cut score — psychometric validation is required before live delivery. Blueprint v1.0.

1. 1

   Register

   Create a free account and start the programme.

2. 2

   Practice

   Online practice test (coming soon).

3. 3

   Training

   Optional

   Online course or classroom — or skip it.

4. 4

   Certification

   Independent exam and certification decision.

Training is optional and does not by itself confer certification. Find a training partner