Why Should I Hire A Data Engineering Consultant
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Owning and operating a business in today’s tech-savvy world opens up a plethora of opportunities for improvement. Through innovative technologies, business intelligence and data science are two fields proliferating the level of insight that owners have into all aspects of their organizations. This is done by collecting, maintaining, and analyzing big data to allow decision-makers to make well-informed decisions every step of the way. To ensure that the data points you in the right direction; however, it is key to have an experienced data science team. To get started down the right path when building your team, consider hiring a data engineer or big data consulting firm.
What is a Data Engineer?
A data engineer or data engineering consulting firm are staples in the IT field and handle the preparation and management of big data for analytical and operational uses. These professional programmers are a key component of any effective data analytics team and often work alongside data scientists and data analysts. While data scientists and analysts focus more on the end result of data analytics, data engineers handle the beginning phases. Essentially, they build and maintain the systems that make it simpler for data scientists and data analysts to translate and analyze datasets.
Likewise, they aid in developing data models, connecting data pipelines, and assist with ETL (Extract, Transform, Load) to ensure that high-quality data is collected, maintained, and able to easily be analyzed by others in the data science field. A data engineer typically refers to an individual employed directly by the organization. However, there are a number of big data consulting services that provide these functions without acting as full-time, permanent employees. This is often a good option for small or midsize businesses with a relatively small amount of data.
What Do Data Engineers Do?
Data engineers and data infrastructure consulting firms, first and foremost, lay the groundwork that makes modern data science and analysis possible. To do so, they use a variety of technical skills and knowledge, such as working with multiple programming languages and SQL database design. Along with the position-specific skill set, they must also know how to communicate with leaders of all departments company-wide to understand which datasets will be most relevant to each. This is especially important when they are working with complex or high volumes of datasets. From there, they establish, build, test, and perpetuate business-specific algorithms to make accessing raw data simpler.
Data engineers and big data consulting firms commonly carry out other tasks like data acquisition, identifying data trends, deploying analytics programs and machine learning, uncovering hidden patterns, and preparing data for predictive modeling. The specific responsibilities of a data engineer will also be influenced by whether they are a generalist, pipeline-centric, or database-centric engineer. Generalists are typically employed by small companies with simple or minimal data loads, while pipeline-centric and database-centric data engineers are reserved for mid-enterprise level ones who use multiple data warehouses and databases.
Data engineers use several tools to fulfill their responsibilities like:
- SSIS
- Informatica
- Snowflake
- Amazon Redshift
- Azure Synapse
- BigQuery
- Tableau
- PowerBI
- Looker
- Kafka
- Spark
- Presto
- Hadoop
Why Do Companies Need Data Engineers?
Analyzing big data provides immense value to companies. It can aid in all components of managing a company, from making important business decisions and improving customer satisfaction to increasing sales. The better the data is, the better the insight that it provides. This is where data engineers come into play. They ensure that the most valuable and accurate big data is collected to be analyzed by data scientists. Without a data engineer, companies may struggle to connect data from multiple sources, and data scientists may be left with data that exists in several formats—making the analysis process more difficult and prone to errors. In the end, engineers can also aid with other processes, such as coding and building.
How Data Engineers Could Help You
Hiring a data engineer can ensure that your company’s data analysis process is simple and effective. While every business will have its own specific needs for data analysis, most can benefit from having one on staff. Some of the most common ways that data engineers can help you include: solving business problems, building and maintaining or improving infrastructure, streamlining data science processes, enhanced business intelligence through data models, pipelines, and machine learning, and continued focus on cutting-edge practices in data science.
What Should I Look For When Hiring a Data Engineering Consultant?
Before you place a job listing or turn to a service, it is crucial to understand what an experienced data engineering consultant looks like. First and foremost, you should determine the type of data engineer that your organization requires. The term “data engineering consultant” is more of an umbrella term that covers a number of different skills in one. Despite this, some candidates will have more analytical skills, while others will be better at building or coding.
A data engineer focusing on analytics will have an education and training that consists of specialties like physics, computer sciences, and math. This type of candidate has a good balance of skills and will be able to handle the majority of tasks related to data sciences. A builder-focused data engineer, on the other hand, is more experienced in information systems and project management. They will be beneficial for organizations in the beginning stages of building or scaling information systems or data pipelines. Data engineers with experience in coding usually have extensive knowledge of programming languages, computer science, and information systems. They are great candidates for organizations that work with big data and real-time data applications.
Regardless of the data engineer candidate’s area of specialty, all data engineers should meet certain requirements like several years of experience with Python, SQL, AWS Redshift, and data visualization tools.
Additionally, communication and research skills are crucial as they will be required to work with various teams in the organization at any given time. Keep in mind that data engineering consulting services or data infrastructure consulting firms are also a solid option if you are looking for experienced and knowledgeable experts in the field.
On your end, be sure that you are upfront with candidates in terms of their responsibilities. For instance, although many data engineering consultants can also act as data scientists, they may not expect this as a part of their role in a mid-sized business. Be sure to clearly define responsibilities and expectations to find the best candidate for your organization. If the position is short-term to build big data infrastructure or improve an existing system, it may be beneficial to turn to a reputable data engineering consulting firm instead. This firm will consist of a team of data specialists with the experience and skills needed to set up or troubleshoot existing infrastructure.
Thanks for reading! If you want to read more about data consulting, big data, and data science, then click below.
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