Getting actionable and valuable data from fully managed cloud services takes a skilled DataOps team. You need data engineers, data scientists, and other senior roles who know cloud platforms, APIs, microservices, and data orchestration tools.
If you were to hire for and build a senior DataOps team internally, it could take 6-18 months, but you can find a team to get started in 4-6 weeks. Before we get to that, let’s review the roles and skills you need to hire for on your DataOps team,, whether you hire internally or find a different option.
Where do fully managed data capabilities and DataOps meet?
Fully-managed data capabilities are cloud-native services that handle the infrastructure and operational aspects of managing your data operations. Managed data services come with scalability, performance optimization, security and compliance, data integration, backup and recovery, automation, and expert support.
DataOps (data operations) combines DevOps practices, Agile methodologies, and Lean manufacturing processes. The objective? To deliver high-quality, reliable data at speed.
When you hire DataOps professionals who know cloud-native architecture and app development, you’re equipping your company to improve data quality, get actionable insights, and scale your data operations faster.
You must find the right combination of roles to build a high-functioning data team.
Crucial roles to hire for your DataOps team
Everyone’s DataOps teams look different. You might find a data scientist that’s also an excellent cloud data engineer. Or your data quality engineer might also be your cloud architect. Ideally, the roles are clearly defined, and no one has too many responsibilities to focus on. But you can hire the mix of skills you need to make fully managed data work at your company without checking off a list.
Here are the prominent roles you’ll want to hire for your DataOps team:
- Cloud data architect designs and maintains the organization's cloud-based data architecture. They can find the right mix of cost-efficiency, security, scalability, and compliance in your data stack.
- Cloud data engineers build and maintain the cloud-based data infrastructure, manage data storage and retrieval, and design data ingestion and ETL pipelines.
- Data scientists create models to extract valuable insights from data, employ statistical analysis, and use machine learning algorithms.
- Data analysts focus on analyzing data to answer specific business questions. They also generate reports and visualizations that help stakeholders understand the data.
- DevOps engineers create an automated, streamlined path for delivering data jobs and ensuring system operations run smoothly.
- Data governance managers oversee data management, ensuring consistency, trustworthiness, and security. They develop data policies and procedures, ensuring compliance with data privacy regulations.
- Data quality analysts/engineers ensure the reliability and high quality of data. They do this by identifying and resolving data quality issues across the business as soon as possible.
- Business intelligence analysts/engineers transform data into meaningful business insights while creating business dashboards, reports, and visualizations.
- Data security analysts/engineers ensure data security and compliance with data protection laws.
- Machine learning engineers ensure the efficient, scalable implementation of machine learning models.
Main technical skills to hire for a DataOps team
It’s great for each individual to have as many of these skills as possible, but it’s the team's combined skills that drive your success. Just make sure you have some combination of these technical DataOps skills.
- Cloud data warehouse knowledge: Familiarity with cloud platforms like AWS, Google Cloud, or Azure, and an understanding of data warehousing concepts.
- Data pipeline expertise: Ability to design, build, and manage data pipelines, including extraction, transformation, and loading (ETL) processes.
- Data programming languages: Proficiency in Python, Java, R, and Scala.
- Database systems: Knowledge of SQL and NoSQL database systems, with an ability to write SQL queries to extract and manipulate data.
- Statistical analysis and machine learning: Knowledge of various statistical analysis techniques and machine learning algorithms to analyze and interpret complex data.
- Data visualization: Familiarity with tools like Tableau, PowerBI, or libraries like Matplotlib and Seaborn to visualize data.
- Continuous Integration/Continuous Deployment (CI/CD): Understanding of CI/CD processes and tools.
- Infrastructure as code: Knowledge of tools like Terraform or CloudFormation.
- Containerization: Experience with Docker and Kubernetes.
- Data profiling: Ability to analyze the data for accuracy and consistency.
- Data governance: Understanding of data governance principles and standards.
- Data security: Understanding of data protection and privacy regulations and standards such as GDPR or CCPA.
- Data quality tools: Experience with data quality tools such as Informatica, Talend, or IBM InfoSphere QualityStage.
- Data management: Knowledge of data management strategies and principles.
- Data observability: Understanding of principles to monitor and understand the health of the data pipelines in real-time.
- Automation: Ability to use tools and scripts to automate repetitive tasks, improving efficiency and reducing errors.
Soft skills that make one DataOps team better than another
Several underlying skills can differentiate a good DataOps team from a great one. These generally revolve around innovating, collaborating effectively, and adapting to new technologies and circumstances.
Here are the primary “soft” skills to hire for in DataOps:
- Strong communication: Effective communication enables cross-functional collaboration, facilitates understanding of requirements, and helps articulate complex data concepts to non-technical stakeholders.
- Problem-solving and critical thinking: The ability to understand complex systems, identify potential problems, and find effective solutions is critical.
- Business acumen: Teams that understand the business's goals and how data can be leveraged for success are far more effective.
- Adaptability: With ever-evolving data technology, a great DataOps team is constantly learning and adapting.
- Proactivity: Constantly looking for ways to improve data quality, speed up delivery, and enhance the overall data pipeline.
- Innovation: Creative thinking about data use, solution development, and process streamlining offers a significant advantage.
- Customer-centricity: Keeping the needs of internal and external customers in mind can help align the team's efforts with the business's needs.
- Knowledge sharing: Active sharing of knowledge and skills boosts overall team competency and cohesion.
- Agile and lean practices: Agile methodologies allow teams to adapt to changes quickly and ensure continuous improvement, while Lean practices help eliminate waste and increase value to customers.
- Automation skills: Automation of processes can significantly reduce manual efforts, minimize errors, and speed up data delivery.
- Data ethics: Understanding the ethical implications of data usage, including privacy, consent, and ensuring the fair and unbiased use of data.
Technical skills are crucial, but these underlying skills can make a DataOps team exceptional and more effective. Make sure to include something like this in your hiring process – whether you build a team internally or partner with an external DataOps team.
How long will it take me to build an excellent DataOps team?
Building an excellent DataOps team from scratch takes a long time – anywhere from 6-18 months. That’s because you have to have a lot of hoops to jump through before you’re building data pipelines and cloud data warehouse solutions. And then you need to keep investing in your data team for years to come.
Here’s what you’ll have to consider to build your team:
- Size and complexity of data operations – Assembling a large team for complex data projects will require more time. These operations might also come with specialized skill requirements.
You might need someone who knows AWS, not Azure. Your DataOps initiatives might not be possible without a Kubernetes expert. These skills could take longer to find.
- Availability of talent – If you're in a region or industry with a lot of competition for senior DataOps professionals, it could take longer to find the right people.
- Recruiting process – The length of your recruiting process will also impact how long it takes to build your team. This includes advertising the positions, screening applications, conducting interviews, making offers, and waiting for new hires to start. Do you have a recruit who knows DataOps? If not, it’s going to take a while.
- Training and onboarding – Once you've hired your team, you'll also need to invest time for training and onboarding. Even experienced hires need time to familiarize themselves with your specific systems and processes.
- Team building and integration – Building a high-performing team isn't just about hiring individuals – it's also about helping them to work together effectively. This involves team-building activities, setting up communication and collaboration tools, and establishing team norms and processes.
It sounds like a long, expensive process, because it is. 41% of companies will skip this process and partner with external DataOps teams to avoid delaying projects.
If you have data initiatives staring you in the face that are impossible to build based on current resources and you have the budget to hire an internal team, it’s worth considering an outside team you can trust.
How do I hire a DataOps team I can trust in 3-6 weeks?
More and more enterprises and venture-funded companies hire modern application development (MAD) service providers to spin up their DataOps team faster. That’s why Codingscape exists. No need to wait 6-18 months before you start building your DataOps initiatives. We can assemble the senior DataOps team you need in 4-6 weeks.
Zappos, Twilio, and Veho are just a few companies that trust us to build software with a remote-first approach. We’ve also built solutions for Amazon and Apple. We know every layer of data operations at scale and love to help companies take advantage of fully managed data in the cloud.
You can schedule a time to talk with us here. No hassle, no expectations, just answers.
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Cole is Codingscape's Content Marketing Strategist & Copywriter.