ETL Developers Hiring Guide

The process that transforms raw data into structured data

ETL (an acronym for Extract, Transform, and Load) is the process of extracting the data from one or more sources, transforming it into a predefined format, and then loading it into a data warehouse system. This process is also called data preparation and is used to structure data for later use.

Extraction

The first step in ETL is called extraction. This means digging/pulling out data from heterogeneous applications and other sources of interest. Most companies extract data first and later filter it according to their own specific needs.

This data is consolidated from those various sources and taken to a staging area. There, you can use it for auditing, backup, and recovery.

You can either perform full extraction or partial data extraction. In full data extraction, all the data from the source is collected without filters. In partial data extraction, only the modified data is extracted from the source. This technique requires the source to keep track of the modified data.

Transformation

Once the data is extracted, it requires mapping and cleansing. That step is called transformation. In this step, data is structured and formatted so you can later use it for analysis.

In this step, engineers perform many custom operations such as sorting, aggregation, and deduplication. Finally, standardization is used on the data to ensure that the final result is compatible with the existing business requirements.

Loading

In this step, the transformed data is taken to a data warehouse system/database from where you can pick up the data for use. In this process, the data is written to the target location. Analysts can then use this data to generate business insights or plug it into data science projects.

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The ETL process requires stakeholders as well as testers, analysts, executives, and engineers to properly define the roadmap. The idea is to get feedback from everyone to truly understand what the company needs from the data it gathers.

After you complete the ETL process, the next process is analyzing the data. This is called business intelligence, and it involves analysts and data scientists. They check and analyze the data and use it to make decisions, all according to the strategy defined in the early stages of the ETL process.

Most companies are now investing in automated tools for ETL to make the whole process efficient and fast. ETL allows you to perform sample data verification and comparison, through which companies can carry out rudimentary analyses. It then generates a visual flow of information.

Through ETL, you can perform impact analysis and track data lineage for historical significance. To perform these tasks, you need specific tools called ETL tools.

ETL In today’s market

ETL is an essential part of data science and BI projects. It allows you to gather data from various sources for analysis and insight. It’s an indispensable first step that eventually allows you to make more informed decisions.

All major companies are now using data science and AI to drive their decision-making. For example, it’s estimated that 75% of the project funding decisions will be done through analytics by 2025. Data science is the future, and ETL processes are a major part of it. Without them, there won’t be any data to leverage.

Issues companies face when hiring ETL engineer

ETL engineers generally develop, automate, support, and design multifaceted applications to extract, transform and load data. This is a complex role, which requires both technical and business expertise. Unfortunately, finding an engineer with both is challenging, as most engineers tend to concentrate only on technical knowledge.

Even if an engineer has the necessary expertise to handle the data, the ETL processes can sometimes be too complex. For example, the source may suffer from a design error, or the data load may be more than expected. In situations like these, an inexperienced engineer won’t be able to write optimized queries for data manipulation. Therefore, you need an engineer who can handle these situations for optimum control over the process.

How to choose a good ETL engineer

An ETL services engineer should have excellent knowledge of data design and architecture. In addition, they should know how to integrate data into backend services and databases.

The engineer should be an expert on data warehousing and should have experience with ETL tools. In addition, they should know UNIX scripting and should be able to run database queries.

Also, you should always go with an engineer who knows how to perform data visualization, since you’ll get better reports for the resulting insights. The selected engineer should be proficient in Python and SQL. In addition, candidates with knowledge of data modeling should be preferred.

Conclusion

ETL processes provide constant access to the latest information and allow faster reporting. Having the correct data can help you make the right decisions and improve your business.

1. What is logging, and how is it done?

Logging is the process of keeping track of all the activities happening before, during, and after the ETL process. All the details such as metadata, timestamps, counts, and discards are added to a flat-file. Notifications can be created for any mismatched data and are sent to respective teams.

2. In ETL, what is the role of impact analysis?

Impact analysis means checking the metadata associated with a particular entity and deciding what part of the warehouse data will be affected. Doing this is important as you should know which tables or columns are affected by a particular data transfer to minimize data disruption.

3. What is an ETL validator?

ETL validators are testing tools that analyze data integration and data migration for ETL processes. They compare records and notify the engineer if something is wrong with the data files.

4. What is data profiling?

It’s a logical analysis of the context, scope, and quality of the data source used for ETL. It’s used to figure out issues in the data source and quality. A good data profile will show the structure of the data and its correlations to help in determining the amount of cleansing required for a particular data file.

5. Which are some of the common ETL tools in the market?

Some of the common ETL tools that companies use are SQL Server Integration Service (SSIS), Elixir Repertoire, SAS Data Management, IBM Infosphere Information Server, and Oracle Warehouse Builder (OWB).

We are looking for motivated ETL engineers who can handle the overall data management design process. They should be able to create functional ETL pipelines based on different requirements. The engineer may also be required to work on data modeling and simulation.

The selected engineer will be part of a global team that fulfills functional requests and meets diverse business specifications. Therefore, the selected engineer should have good communication skills to collaborate with multiple stakeholders.

Responsibilities

Work on data warehousing, data integration, data migration, and business intelligence
Create software modules for mappings and transformations.
Work on data design and functionality
Maintain scalability and maintainability of data
Work on ETL pipelines and fix issues associated with them.
Gather business requirements from stakeholders and perform data profiling
Follow best industry practices and standards
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Skills and Qualifications

Knowledge of ETL processes. Should have prior experience with ETL tools.
Experience in designing functional ETL code modules
Proven experience with data mapping and data warehousing. Should also have experience in data modeling
Deep understanding of SQL and query optimization.
Experience with code versioning tools (Git and Jenkins)
Knowledge of code testing and debugging
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