Data Warehouse Developers Hiring Guide

Big Business Depends on Big Data

Your business depends on data. With each passing year, that becomes more and more obvious. No matter the size of your company, you need the information to keep you moving forward. That information might consist of customer details, sales data, employee records, supply chain records, products, clients, location data, or trends. The list of the data your company needs can get quite endless.

And the longer you’re in business, those stores can grow exponentially. Where do you keep that data? Once your business grows beyond a simple server or collection of servers to house your data, you’ll realize you need something serious, such as a data warehouse.

The very term probably conjures massive buildings housing rows and rows of servers that are all clustered together to keep all of that precious data safe. Although that’s an intriguing bit of imagery, such a concept only holds true for the largest companies.

For your business, a data warehouse is actually a large collection of business data that can be used to help your company make informed decisions. This concept has been around since the ’80s. At that point it had become obvious data was more than just a means to house information, but a way to help make important decisions that can reveal business intelligence.

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Job Description

More about Business Inteligence

Business Intelligence (BI) comprises both the strategies and the technologies that are used by enterprise-class companies to analyze collections of data. The analysis of such data has become key for corporations to strategize for the future. And given how competitive the world of business has become, every advantage (regardless of how small) can mean the difference between success and failure.

Although you might think you can get by with the traditional database and web-based GUI, there are 2 very important benefits to migrating to a data warehouse:

Better data - you'll be able to collect more consistent and relevant information from a particular source.
Faster decisions - because the data stored in a warehouse is in a more consistent format, the systems you use for analysis can arrive at decisions much faster.

It's important to understand that a data warehouse isn't just a single collection of data. Rather, a data warehouse is a collection of stored databases. That means you could have different databases from a variety of sources or each of which houses data specific to such things as regions, clients, or products.

Data Lake or Not?

You might have heard the term "data lake." Although this is another important concept, you need to understand that a data warehouse and a data lake are 2 very different things. A data lake is a collection of multiple types of data, including raw, unstructured, and structured. These different sets of data are stored in their raw format until they are needed. A data warehouse, on the other hand, stores data in organized files and folders that is ready to be used by analytics tools.

What You'll Need to Build a Data Warehouse

First of all, a data warehouse isn't something your IT admin can download and point-and-click their way to deploying. This is a very complicated, involved, and lengthy procedure. That means you'll need to have the required staff to do in-depth research and who fully understand how data works.

So the first thing you're going to need to do is to collect your data, which can come from nearly any source. This can be ad performance, website or app tracking, e-commerce, marketing, customer relations, customer support, or financial data. You can collect that data with tools like Google Analytics, Snowplow, Heap, your company HRM tool, or Zendesk. That means you're going to need staff trained in the extraction of data from those platforms.

Once you have your data collected, you'll then need to turn to a company that offers data warehouse solutions. Yes, you could always build your own in-house data warehouse, but why reinvent the wheel? Some of the more startup-friendly data warehouse services include:

Of the above services only Panopy offers built-in, easy-to-use connectors for nearly any type of data you've collected. This makes Panoply the most user-friendly, Snowflake can get expensive, and Amazon Redshift can be the most complicated. However, if you expect your data warehouse to grow fast and large, Amazon certainly has the infrastructure to house any size data warehouse you need.

Next, you'll need the right ETL tool. ETL stands for Extract, Transform, Load. This will only be necessary if you opt to go with a data warehouse solution that doesn't include a connector for your data. If that's the case, you'll need to turn to the likes of Singer, Stitch, Blendo, or Fivetran. Naturally, you'll need staff members capable of using these tools.

Finally, you'll need to employ the right analytics tools, such as Google Data Studio, Looker, Metabase, or Mode.

Once you have all of those pieces together, your data warehouse is ready to be used.

Conclusion

As you can probably tell, creating a data warehouse isn't an easy task. That's why you'll need to make sure to hire data warehouse developers who are capable of putting these technologies together, so your business can make the most out of your data and raise your business intelligence game to the next level.

What is a data warehouse?

A data warehouse is a collection of data that is used as a management decision and/or business intelligence support system.

What is a fact table?

A fact table contains the measurement of business processes, as well as foreign keys used for dimension tables.

What are the 4 stages of data warehousing?

Offline operational database
Offline data warehouse
Real-time data warehouse
Integrated data warehouse

What does OLTP stand for?

On-Line Transaction Processing

What does OLAP stand for?

Online Analytical Processing

What is the difference between View and Materialized View?

A view is a virtual table that takes the output of a query to be used in place of tables, while a materialized view is indirect access to the table data by storing the results of a query in a separate schema.

What are non-additive facts?

Non-addictive facts can’t be summed up for any of the dimensions present in the fact table.

What are the 3 types of Slowly Changing Dimensions?

SCD 1 - a new record replaces the original record
SCD 2 - a new record is added to the existing customer dimension table
SCD 3 - original data is modified to include new data

You will be responsible for planning, connecting, designing, scheduling, and deploying our data warehouse systems. Other duties will include the development, monitoring, and maintaining of ETL processes, reporting applications, and data warehouse design.

Responsibilities

Plan, create, coordinate, and deploy company data warehouses.
Design any necessary end-user interfaces or train users with third-party tools.
Develop best practices for data loading and extraction.
Develop and manage every aspect of data architecture, data modeling, and ETFL mapping solutions within a structured data warehouse environment.
Develop and/or deploy the necessary reporting applications.
Develop and implement ETL routines.
Support the development and validation required through the lifecycle of the data warehouse and business intelligence systems.
Maintain user connectivity and provide security for the data warehouse.
Monitor the data warehouse and business intelligence systems performance.
Manage multiple projects at once.

Skills and Qualifications

Knowledge and understanding of the software development life cycle.
Advanced knowledge and experience (minimum 5 years) in relational databases and the SQL query language.
Experience in database design and modeling for data warehouses.
Experience with business intelligence applications (including relational database structures and normal forms).
Analytical and troubleshooting skills with complex technical subjects and tasks.
Minimum 5 years experience with SQL Server, TSQL, SSAS, SSIS, SSRS, SharePoint Development Studio, and Oracle DBMS.
Superior analytical skills with a good problem-solving attitude.
Fundamental understanding of version control systems (such as Git).
Solid problem-solving skills.
Excellent written and verbal communication.
Good organizational skills.
Ability to work as part of a team.
Attention to detail.
Understanding the nature of asynchronous programming and its quirks and workarounds
A positive attitude.