Back in 2006, data was declared "the new oil." In 2023, data may as well be declared the new hydrogen cell, solar farm, or fusion reactor. Data is the fuel that powers artificial intelligence, machine learning, and predictive analysis, and, according to Forrester, how a business uses its data is "one of the defining factors that will make or break a company in the age of insight and AI."
Data engineers and data scientists have quickly become some of the most in-demand tech roles, and hiring for these roles is increasingly challenging:
The demand for data engineers has been on a sharp rise since 2016. Years after that, we find a shortage in the number of skilled data engineers and an increase in the number of jobs. As per a 2021 report by DICE, data engineer is the fastest-growing job role and witnessed 50% annual growth in 2022.
— Arif Alam, "Roadmap to Becoming a Data Engineer"
Because of these shortages, companies that need data engineers and data scientists are increasingly supplementing their teams through staff augmentation.
Staff augmentation can help enterprises scale up for multiple data-driven use cases:
When it is time to scale up your organization’s data engineering or data science capability, how do you find people qualified to help?
To find the best team to solve your data puzzle, you'll first want to understand what sort of puzzle you're up against and what job roles can solve it. Though data engineers and data scientists often work side by side, the roles aren't the same. Make sure you're hiring the right people for the right job.
A data engineer (DE) specializes in collecting, consolidating, storing, and structuring complex data sets. If your goal is to acquire or prepare datasets, build data pipelines, ensure data quality and reliability, or optimize data performance, then you probably need data engineers.
A data scientist (DS) is trained to analyze and model this aggregated data to find patterns. If you are trying to mine your company data for actionable insights, create predictive analytics, or develop and deploy machine learning models, or if you're looking for ideas for how your data can improve your bottom line, you will probably want data scientists.
It might stand to reason that a large team of data engineers will work more quickly than a smaller team, but simply throwing bodies at a problem won’t necessarily solve it.
Effective staff augmentation isn't about quantity; it is about quality. Data problems are complex, and because of that complexity, the people working together on a data project need to communicate clearly. A small team of seasoned data engineers can efficiently coordinate and work together, but communication becomes exponentially more difficult as the team grows. You don’t want your project to turn into a game of “Whisper down the lane.”
How big is too big? Some jobs benefit from the added firepower of more people — as long as they communicate clearly and regularly and maintain quality control.
There are some jobs that you can safely outsource halfway around the world: you describe what you need, you go to sleep, and when you wake up, poof, the job is done, like magic.
Data jobs are different.
To get the best results from staff augmentation, you'll want to be able to work closely with the people you hire. They should feel like part of your team.
You want them to understand your business, your goals, and your technology — and you want to be able to correct any misunderstandings as quickly as possible.
This doesn't mean that everyone needs to sit in the same office — but in our experience, data projects are most successful when everyone involved is in similar time zones, with concurrent or at least overlapping workdays. This way, you can check in regularly to ensure your expectations are being met — and if an issue comes up, it can be resolved in real-time.
The most important thing you need from a data team is knowledge. When a problem arises, you want to know that your team has the skills to deal with it.
At WillowTree, we have assembled a world-class team, hiring less than 2% of applicants yearly. Our global practitioners have advanced degrees in statistics, machine learning, data engineering, and computer science and a track record of driving clients' data and Al efforts across use cases and sectors.
Collectively, we have worked with all the major databases (PostgreSQL, Redshift, ElasticSearch, MongoDB, Neptune), machine learning frameworks (Pytorch, Tensorflow, scikit-learn, spaCy, HuggingFace, pytesseract), big data and ETL tools (Spark, Hadoop, hive, Airflow, Databricks, dbt, Pentaho, Azure Data Factory, AWS Glue), business intelligence software (Tableau, PowerBI, Looker, Dash), and MLOps (Kubeflow, MLFlow, AWS Sagemaker).
At WillowTree, the first and most important part of our process is matching client needs. We learn about your organization and provide data engineers and scientists with the technical expertise to work successfully in your team.
Throughout the process, we use regular check-ins. When a WillowTree engineer joins a client team, we set up regular recurring meetings to ensure your expectations are met and exceeded.
Finally, we rely on whole-team expertise. When working with a WillowTree engineer or data scientist, clients should expect access to the combined capability of our team of hundreds of cross-functional specialists across strategy, design, development, and marketing, in addition to those in data, generative AI, and machine learning.
If you’re looking to augment your staff with experienced, skilled data engineers and data scientists, WillowTree’s data and AI capabilities and deep bench of world-class practitioners can handle an enterprise’s most complex data challenges.
Schedule a call to increase your AI talent pool and capacity for immediate growth. Make our team your team. Get in touch.