Data analytics is the lifeblood of any successful business. Getting the technology right can be challenging, but building the right team with the right skills to undertake data initiatives can be even harder.
Successfully deploying big data initiatives requires more than data scientists and data analysts. It requires data architects, who design the blueprint for your enterprise data management framework, as well as data engineers, who can build that framework and the data pipelines to bring in, process, and create business value out of data.
Data architect roles and responsibilities
Data architects are senior visionaries who translate business requirements into technology requirements, and define data standards and principles. They typically have years of experience in data design, data management, and data storage.
Typical data architect responsibilities include:
- Translating business requirements into technical specifications, including data streams, integrations, transformations, databases, and data warehouses.
- Defining the data architecture framework, standards, and principles, including modeling, metadata, security, reference data such as product codes and client. categories, and master data such as clients, vendors, materials, and employees
- Defining reference architecture, which is a pattern others can follow to create and improve data systems.
- Defining data flows, i.e., which parts of the organization generate data, which require data to function, how data flows are managed, and how data changes in transition.
- Collaborating and coordinating with multiple departments, stakeholders, partners, and external vendors.
Data engineer roles and responsibilities
Data engineers are responsible for managing and organizing data, while also keeping an eye out for trends or inconsistencies that will impact business goals. Data engineers also design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineers are typically skilled in technologies such as Hadoop, Spark, and other tools from the open-source big data ecosystem, and at programming in Java, Scala, or Python.
Typical data engineer responsibilities include:
- Developing, constructing, testing, and maintaining architectures.
- Data acquisition.
- Developing data set processes.
- Identifying ways to improve data reliability, efficiency, and quality.
- Deploying sophisticated analytics programs, machine learning, and statistical methods.
- Preparing data for predictive and prescriptive modeling.
- Using data to discover tasks that can be automated.
Benefits of certification
If you’re looking to get an edge for either of these essential data roles, certification is a great option. Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers you have the right skillset for the job.
Below is our guide to the most sought-after data engineer and data architect certifications to help you decide which is right for you. Not finding what you’re looking for? Check out our list of data analytics certifications.
If you would like to submit a big data certification to this directory, please email us.
The top 11 data engineer and data architect certifications
- Amazon Web Services (AWS) Certified Data Engineer – Associate
- Arcitura Big Data Architect
- Cloudera Data Engineer
- Data Science Council of America (DASCA) Associate Big Data Engineer
- Data Science Council of America (DASCA) Senior Big Data Engineer
- Databricks Certified Data Engineer Professional
- Google Professional Data Engineer
- IBM Certified Solution Architect – Cloud Pak for Data v4.x
- Microsoft Certified: Azure Data Engineer Associate
- SAS Certified Data Integration Developer
- SnowPro Advanced Data Engineer
Amazon Web Services (AWS) Certified Data Engineer – Associate
The AWS Certified Data Engineer – Associate certification showcases the ability to design data models, manage data life cycles, and ensure data quality. It validates skills and knowledge in core data-related AWS services, the ability to ingest and transform data, and orchestrate data pipelines while applying programming concepts. This certification is valid for three years from the date earned.
Organization: Amazon Web Services
Price: $150 registration fee for exam
How to prepare: Amazon offers a four-step plan to prepare for the exam.
Arcitura Big Data Architect
Arcitura’s Big Data Architect certification validates knowledge of big data platform technology architecture, and big data application architecture within IT enterprise and cloud-based environments. Attaining the certification requires a passing grade on the complete Big Data Architect Certification Exam or a passing grade on the partial Big Data Architect Certification Exam, and attaining the Big Data Professional Certification.
Organization: Arcitura
Price: $249
How to Prepare: Arcitura recommends taking the modules in its Big Data Architect certification track.
Cloudera Data Engineer
The Cloudera Data Engineer certification verifies the holder has the skills and knowledge required by data engineers using the Cloudera platform. The certification validates that the holder knows how to work proficiently in designing, developing, and optimizing data workflows using Cloudera tools. Candidates have a strong grasp of data modeling for efficient storage, including formats, partitioning and schema design, and Apache Iceberg. They’re also proficient in security configuration, monitoring, troubleshooting, and cloud integration for Cloudera clusters using Spark and Airflow.
Organization: Cloudera
Price: $330
How to prepare: Cloudera offers an exam guide, which suggests candidates take three Cloudera Educational Services courses: Preparing with Cloudera Data Engineering, Advanced Spark Application Performance Tuning, and CDP Iceberg Integration.
Data Science Council of America (DASCA) Associate Big Data Engineer
The vendor-neutral DASCA Associate Big Data Engineer certification demonstrates knowledge of popular big data platforms, including Hadoop and Spark, and knowledge of proprietary and open-source developer tools (including HBase, Hive, Pig, and HiveQL). It requires passing a 75-question online exam, and there are three candidacy tracks that vary based on level of education and work experience.
Organization: Data Science Council of America
Price: $625
How to prepare: Registration for the program includes a full DASCA Certification Preparation Kit.
Data Science Council of America (DASCA) Senior Big Data Engineer
DASCA’s Senior Big Data Engineer certification is a step up from the associate credential, intended for experienced professionals. It requires passing an 85-question online exam. There are four candidacy tracks that vary based on level of education and work experience.
Organization: Data Science Council of America
Price: $750
How to prepare: Registration for the program includes a full DASCA Certification Preparation Kit.
Databricks Certified Data Engineer Professional
The Databricks Certified Data Engineer Professional certification assess a candidate’s ability to use Databricks to perform advanced data engineering tasks, including an understanding of the Databricks platform and developer tools like Apache Spark, Delta Lake, MLflow, and the Databricks CLI and REST API. It validates the holder’s ability to build optimized and cleaned ETL pipelines, model data into a lakehouse using knowledge of general data modeling concepts, and ensure data pipelines are secure, reliable, monitored, and tested before deployment. Recertification is required every two years to maintain status.
Organization: Databricks
Price: $200
How to Prepare: Databricks offers an exam guide and an instructor-led Advanced Data Engineering with Databricks course, as well as a self-paced course via Databricks Academy.
Google Professional Data Engineer
The Google Professional Data Engineer credential certifies the ability to design, build, operationalize, secure, and monitor data processing systems. It requires passing a two-hour, multiple-choice and multiple-select certification exam. The exam has no prerequisites, though Google recommends candidates have three or more years of industry experience, including one or more years designing and managing solutions using Google Cloud Platform. The exam is available in English and Japanese and may be taken as an online-proctored exam from a remote location, or as an onsite-proctored exam at a testing center.
Organization: Google
Price: $200 registration fee
How to prepare: Google offers an exam guide and on-demand or instructor-led training.
IBM Certified Solution Architect – Cloud Pak for Data v4.x
This certification validates an individual’s ability to design, plan, and architect a data and AI solution in a hybrid cloud environment. A certified architect can lead and guide the implementation and operationalization of a solution that may include data governance, analytics, data science, machine learning, and AI. It requires passing a test that consists of six sections containing a total of 63 multiple-choice questions.
Organization: IBM Professional Certification Program
Price: $200
How to prepare: IBM offers a sample test and study guide. It also offers an assessment exam through Pearson VUE. There’s also a learning path that takes about 13.5 hours to complete.
Microsoft Certified: Azure Data Engineer Associate
The Microsoft Certified: Azure Data Engineer Associate certification demonstrates understanding of common data engineering tasks to implement and manage data engineering workloads on Microsoft Azure using a number of Azure services. Candidates should have subject matter expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a schema for building an analytics solution. Candidates must have knowledge of SQL, Python, and Scala and should have proficiency in Azure Data Factory, Azure Synapse Analytics, Azure Stream Analytics, Azure Event Hubs, Azure Data Lake Storage, and Azure Databricks. The certification must be renewed every 12 months.
Organization: Microsoft Learn
Price: $165
How to Prepare: Microsoft recommends taking the Data Engineering on Microsoft Azure course.
SAS Certified Data Integration Developer
This certification program is for individuals seeking to validate their data integration development skills in the SAS 9 environment. The program focuses on defining architecture of the platform for SAS Business Analytics, creating metadata for source and target data, working with transformations, and more. The program requires passing a certification exam administered by SAS and Pearson Vue.
Organization: SAS Global Certification Program
Price: $180
How to prepare: SAS offers an exam guide, The SAS Data Integration Studio: Fast Track course, sample questions, and practice exams.
SnowPro Advanced Data Engineer
The SnowPro Advanced Data Engineer certification validates advanced knowledge and skills in data engineering principles using Snowflake. It demonstrates 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; and evaluate performance metrics. Candidates should have two or more years of hands-on Snowflake practitioner experience in a data engineering role.
Organization: Snowflake
Price: $175 per exam attemptHow to Prepare: Snowflake recommends either its instructor-led Snowflake Data Engineer Training or the SnowPro Advanced: Data Engineer Certification Exam Study Guide.
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