In today’s data-driven business landscape, few roles are more crucial than an ETL developer. These software specialists are responsible for designing complex data storage systems that are critical to business intelligence. In short, they turn Big Data into Big Wins.
ETL stands for extract, transform, and load, referring to the general procedure for transferring data from multiple sources to a single data warehouse. Data is then used by analysts to make better business decisions.
Needless to say, if you need an ETL developer, it’s vitally important to hire the right one for your company. It’s no secret that data-savvy businesses outperform their competitors.
Recruiters should be mindful that ETL tools can vary widely. For this reason, you need to tailor the recruitment process to your organization’s specific ETL needs where possible.
We recommend using a skills assessment before the interview, in order to shortlist your best candidates and concentrate on them only—and not spend time interviewing unsuitable applicants.
Below, you’ll find 25 of the most important ETL interview questions to help you make the best hire. We’ve split the questions into three groups—beginner, intermediate, and advanced—for different experience levels.
You’ll also find sample answers for your reference, so that you can evaluate applicants’ skills confidently, even if you don’t have any ETL development skills yourself.
If you’re hiring for an entry-level ETL developer with up to 2 years of experience, consider using these beginner interview questions.
While the abbreviation implies a neat, three-step process, ETL actually encompasses more steps. ETL refers to data extraction from different sources, its transformation and loading into a data warehouse, and the eventual analysis of the warehouse data.
In short, ETL comprises the transportation of data across all four areas: extraction, transformation, loading, and analysis.
Data warehousing is a core component of business intelligence. By bringing different data sources under a single, unified library, analysts can work more efficiently, get more in-depth insights, and spot patterns across different datasets.
Ultimately, it helps businesses be more competitive by improving their decision-making process.
ETL transforms data before it’s loaded into the target system, while ELT transforms data within the data warehouse. Out of the two, ELT is generally considered the better solution for large amounts of data, offering a more flexible and agile way of working with data.
Partitioning refers to the division of large datasets into smaller, more manageable areas based on shared characteristics. Its purpose is to make the data warehouse easier to navigate and improve query processing performance.
There are a number of different ETL software tools on the market, but they all share the same purpose of data integration. Some of the most popular ETL tools are:
IBM InfoSphere DataStage
Oracle Data Integrator
Microsoft SQL Server Integration Services (SSIS)
Facts are quantitative pieces of information about a business, such as sales numbers or account balances. They’re stored in a fact table. There are three different types of facts:
Non-additive facts cannot be summed up across any dimension in the fact table, such as a percentage
Semi-additive facts can be summed up for some, but not all, dimensions in the fact table, such as a headcount
Additive facts can be summed up across all dimensions in the fact table, such as sales
Fact tables contain measurements and metrics about a business. Dimension tables are adjoined to fact tables by a foreign key and show the descriptive characteristics of the facts within them. While fact tables are granular, dimension tables are wordy and detailed.
Regular testing is an essential part of the ETL process and ensures that data arrives in the analytics warehouse smoothly and accurately.
ETL testing can be performed in the following ways:
Review primary sources to make sure they have extracted without any data loss
Verify that the data has been transformed into the appropriate data type for the warehouse
Check that the warehouse accurately reports cases of invalid data
Document any bugs that occur during the ETL process
Next, you’ll find some intermediate ETL developer questions. Use these when recruiting for mid-level roles where applicants already have some hands-on experience working with ETL.
Once data has been successfully transported into the data warehouse, analysts typically use third-party Business Intelligence (BI) applications such as Tableau to turn the raw data into graphs and charts, based on which business decisions can be made.
Some of the latest ETL tools are equipped with their own data analysis mechanisms.
There are several ETL testing methods, each one serving a specific purpose at different points in the ETL process. Some of the most common types of ETL testing are:
Production validation: Data in the target system is compared against the sources for validity purposes
Source to target count testing: The number of loaded records in the data warehouse is checked for consistency against the expected record count
Performance testing: The loading process is tested to make sure it takes place within the expected time frames
Data transformation testing: Data in the target system is checked to verify that it has been correctly transformed in line with business requirements
Snapshots are read-only copies of data from the master table. Before a change to the data warehouse is made, a snapshot is taken and stored locally for reference. This works to preserve records as they were before the change was made.
Each snapshot has three components:
The time it was taken
A key to identify the snapshot
The data relating to the key
Data profiling is the process of reviewing source data to identify its structure, quality, and interrelationships.
It’s an important precursor to the analysis stage, as it ensures that the appropriate source data is loaded into the data warehouse and that it is transformed in line with the business requirements.
ETL testing can reveal a wide variety of problems. Some of these include:
Loss of data between the source and the target system
Lack of consistency between datasets
Lengthy query processing
System crashes due to the scale of the data warehouse
Cosmetic bugs related to font, color, or alignment
Most data warehouses comprise three separate areas. These are:
The staging area, where data is extracted from various sources and processed
The data integration area, where data from the staging area is transformed (sometimes called the OLAP server)
The access area, where transformed data is retrieved by users for analysis
The staging area is the landing zone for data extracted from sources and sits between the source and target in the ETL process.
Here, the data is cleansed and modified before it is transferred to the data warehouse. This is a more efficient alternative to transforming data in the target system itself.
Initial load refers to the process of loading all data from primary sources into the target system for the first time. Once this has been completed, all subsequent loads into the system will be incremental loads, where only new or modified records are brought in.
Below, you can find nine advanced ETL developer questions. You can use these to assess the skills and knowledge of senior ETL developers who have plenty of experience.
It’s important to identify potential challenges early on in the ETL process to avoid bottlenecks further down the pipeline. Some of the most common problems and challenges of ETL testing are:
Loss, corruption, or duplication of data during transportation
Underperformance caused by large volumes of historical data
Unachievable business requirements
Limited availability of source data
Outdated ETL tools
ETL testing is a demanding process that should be completed in the following order:
Define the business requirements: Liaise with the client to understand their reporting needs and define the scope of the project
Validate data sources: Perform a data count check and ensure check keys are in place
Design ETL logic: Design the mapping sheet, SQL script, and transformational codes
Extract source data: Identify any bugs during the extraction phase
Transform data: Make sure data is transformed consistently
Load data: Perform a record count check and verify the accuracy of the loaded data
Review process: Verify the validity, layout, and export functionality of the summary report
File test report: Share test results with the relevant stakeholders
A good ETL tool makes the data integration process more efficient and user-friendly. Some particularly useful ETL features are:
Cloud compatibility, allowing for greater flexibility and better handling of massive datasets
Third-party integrations to connect with ERP platforms and BI tools
Automatic code generation to reduce the risks of human errors and speed up development
Intuitive interface to improve user navigation
Sophisticated debugging tools, which reduce disruption to data flows
ETL is an important early phase of the data mining process. After data sources have been identified and business requirements are set, ETL is performed to bring all historical data under a single, consistent system.
From here, data is analyzed and modeled using BI tools. Data scientists are then able to evaluate the data to draw conclusions about business decisions.
The two main types of partitioning in ETL are:
Hash partitioning: Rows are assigned using a hash key, meaning partitions depend on the specified hashing algorithm
Round-robin partitioning: Rows are assigned in a round-robin manner, meaning each partition contains approximately the same number of rows
Regression testing is used after developing functional repairs to the data warehouse. Its purpose is to check if said repairs have impaired other areas of the ETL process.
Regression testing should always be performed after system modifications to see if they have introduced new defects.
Data purging is the process of permanently deleting obsolete data from the data warehouse. For example, data may be purged once it becomes ten years old. This is done to free up space on the server and improve performance.
Data purging is usually accompanied by archiving, where data is moved to a separate storage device for long-term retention, typically for legal purposes.
There are several key differences between connected and unconnected lookups:
Connected lookups return multiple columns from the same row, whereas unconnected lookups return one column from each row
Connected lookups receive values directly from the mapping pipeline, whereas unconnected lookups receive values from a separate transformation
Connected lookups use a dynamic or static cache, whereas unconnected lookups only use a static cache
Lookup transformation is used to retrieve values from a data source based on specific lookup conditions. There are a few scenarios when this may be necessary, for example, to:
Update a dimension table
Check if records already exist in a table
Find a specific value from a table
Interviews are just one part of what should be a rigorous, multi-stage candidate selection process. It’s important to draw on other recruitment tools and techniques when hiring your next ETL developer so that you can gain a more rounded view of each candidate’s skill set.
Interviews work best at the back end of the selection process—using skills assessments and tasks beforehand allows you to rule out underqualified applicants at an earlier stage, helping you save time by only interviewing your best candidates.
Here is an outline for an effective ETL hiring process:
Job posting: Make sure to state the ETL tools used by your organization in the job advert and list the responsibilities of the role
Resume screening: Sift through CVs, shortlisting candidates that might have the right ETL skills required for the position
Skills assessment: Invite shortlisted candidates to complete an online skills assessment of up to five different tests—you could assess their ETL skills, cognitive abilities, or even their personality traits or culture add potential
Screening call: Have an informal chat with candidates to see if your expectations and goals align
Role-specific task: Present candidates with an ETL task similar to what they would do in the role, such as creating an automated data cleansing process
Interview: Draw on interview questions above, but also remember to assess the candidate’s soft skills
Reference check: Perform checks on the candidate’s background and references
Final decision and job offer: Make your final decision based on skills, personality and culture add potential before making a job offer
The highly specialized nature of ETL makes it even more important to include hands-on programming skills assessments and role-specific tasks as a part of the selection process.
Work samples are one of the most effective predictors of a candidate’s likely performance and are used by many of the world’s biggest corporations, including Google. Structured interviews, where you’re asking all interviewees the same questions in the same order will help you accurately evaluate their performance and limit bias.
If you’ve already got a good idea of the candidate’s ETL expertise by the time the interview rolls around, ask a few specific ETL questions and focus on the candidate’s soft skills and personality fit. Any hiring decision should take into account attitude just as much as aptitude.
By using interview questions alongside role-specific assessments, you can create a bulletproof selection process capable of unearthing the best ETL developer for your organization.
Remember to tailor the recruitment process to your company’s ETL needs and the specific requirements of the role. This will help you to find a candidate with the right skills and, ultimately, maximize your chances of making a mutually beneficial long-term hire.
Try TestGorilla for free today and start making better hiring decisions.
To address its increased recruitment needs and influx of applicants for roles that include customer support and leadership, Dyninno Group implemented TestGorilla. See how the Dyninno Group of companies improved candidate screening and recruitment productivity by 400%.
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