DataOps combines practices from Agile development, DevOps, and data engineering to improve the quality, speed, and reliability of data analytics. It requires the collaboration of data scientists, data engineers, IT operations, data analysts, and other stakeholders to streamline the process of gathering, processing, and analyzing data. Hiring a DataOps team with the right combination of technical skills and business acumen is challenging, but possible if you know what to look for.
Start with finding the key roles to hire and understanding how they all mesh together. You need to review a list of business considerations and answer some questions. Use this to establish your “why” for the DataOps team and make sure you have buy-in from your stakeholders. Then equip the people you hire with the right tools and maintain them.
Here’s an overview of everything you need to know to build an effective DataOps team.
Strategic challenges to building a DataOps team
Let’s start with the complex strategic challenges to building an effective DataOps team. The “Why” you come up with will define how you navigate these and ultimately decide on the roles you need to hire. It’s easy to think that you can just hire one of every role and have a functional DataOps team, but that’s not how building a successful team works.
You want to make sure you know exactly what needs to be done and which skill sets you need to serve the business.
Here’s where we’d get started:
- Define clear roles and responsibilities. Determining specific roles like Data Engineers, Data Analysts, Data Scientists, Infrastructure Engineers, and DataOps Managers, as well as their respective duties, can be challenging. They have interrelated responsibilities and skills. Clearly outline each role's tasks and expectations for effective team operation.
- Find and attract the right talent. Hiring individuals with the required technical skills, domain knowledge, and soft skills can be difficult, especially in a competitive job market. To attract top talent, your company needs to offer competitive compensation, a positive work environment, and opportunities for growth and development.
- Ensure alignment with business goals. Aligning the DataOps team's objectives with the organization's broader goals is critical for success. This requires effective communication and collaboration between the DataOps team and other departments, such as data science, IT, product, software development teams, and business intelligence.
- Establish effective processes and workflows. Developing efficient data pipelines, ensuring data quality and security, and implementing Agile and DevOps methodologies can be complex. The DataOps team must continuously refine and optimize processes and workflows to maintain data quality, security, and compliance.
- Scale team and infrastructure. As the organization's data needs grow, it can be challenging to scale the DataOps team and infrastructure accordingly. This may involve hiring additional team members, investing in new tools and technologies, or adapting existing processes and workflows.
- Manage tools and technology. Selecting the right tools and technologies for the team can be a daunting task, as there are numerous options available. Ensuring that these tools integrate well with existing systems and can scale to meet future needs is essential.
- Ensure data security and compliance. DataOps teams need to be aware of and adhere to relevant data privacy, security, and compliance regulations. This may involve implementing data protection policies and procedures and staying up-to-date with ever-changing regulatory requirements.
- Measure success and track progress. Defining and tracking key performance indicators (KPIs) can be challenging but is necessary to evaluate the team's performance and progress toward organizational goals. Regularly reviewing and adjusting these KPIs is essential to ensure continued success.
- Budget constraints. Building and maintaining a DataOps team requires financial resources to recruit, train, and retain talent and invest in the necessary tools and infrastructure. Balancing the team's needs with budget constraints can be a significant challenge.
Key roles and salary ranges for a DataOps team
Once you have all those challenges addressed, or at least on your radar, it’s time to start the hiring process. That can take 6-18 months depending on your recruiting resources, talent availability, and budget constraints. And it helps to have a clear idea of all the DataOps roles and their unique skills and expertise before you make your list and start writing job descriptions.
The key roles and their responsibilities typically include the following:
- Data engineers: Estimated salary range of $70,000 - $130,000
They’re responsible for designing, building, and maintaining the data pipelines that collect, process, and store data from various sources. Data engineers also ensure that data is clean, accurate, and available for analysis by working with data ingestion, transformation, and storage technologies.
- Data scientists: Estimated salary range of $80,000 - $200,000
They analyze data to extract insights, develop predictive models, and provide actionable recommendations. Data scientists collaborate closely with data engineers to ensure the data they need is readily accessible and of high quality. They also work with business stakeholders to understand their needs and communicate findings effectively.
- DataOps engineers: Estimated salary range of $70,000 - $120,000
These engineers focus on implementing DataOps practices, such as automation, CI/CD, and monitoring. They work closely with data engineers and data scientists to streamline the development, testing, and deployment of data pipelines and machine learning models. DataOps engineers also help establish best practices and guidelines for the team to follow.
- DataOps manager: Estimated salary range of $90,000 - $150,000
A DataOps Manager oversees the DataOps team and coordinates efforts to ensure efficient and effective data operations. They are responsible for setting team goals, managing resources, and facilitating collaboration between different stakeholders.
- Data analysts: Estimated salary range of $60,000 - $90,000
They’re responsible for analyzing and interpreting data to answer specific business questions and generate insights. Data analysts work closely with data scientists and business stakeholders to understand requirements and deliver reports, dashboards, and visualizations.
- Data stewards: Estimated salary range of $70,000 - $110,000
A unique role in DataOps, they’re responsible for ensuring the quality, consistency, and security of data throughout its lifecycle. Data stewards work with data engineers and data scientists to establish data governance policies, monitor compliance, and resolve data quality and management issues.
These roles can vary depending on your organization's size, industry, and specific needs. In some cases, individuals may take on multiple roles or collaborate as a cross-functional team to drive DataOps success. One of the biggest things to consider when building your team is this crossover in skills and understanding what overall mix you need to be successful.
Main things to consider when hiring for DataOps roles
DataOps team members need to know the technical side of their work and be great communicators. This team makes sure your data infrastructure is efficient, reliable, and scalable. That means they’ll have to work closely with people across IT, DevOps, and the business at large.
In other words, it’s about more than data—your team needs a wide range of qualities to be successful.
When building a DataOps team, consider these main factors:
- Technical skills: Look for candidates with a strong background in relevant technical skills, such as programming languages (e.g., Python, R, or Scala), data integration and transformation tools (e.g., Apache NiFi, Talend, or Informatica), big data technologies (e.g., Hadoop, Spark, or Flink), analytics pipelines, and database systems (e.g., SQL, NoSQL, or NewSQL).
- Collaboration and communication: Strong communication and collaboration skills are essential for DataOps team members, as they will need to work with other teams (business intelligence, IT, and marketing) to deliver data-driven solutions.
- Problem-solving and critical thinking: DataOps professionals should be able to analyze complex problems, develop innovative solutions, and make data-driven decisions to ensure smooth data operations.
- Adaptability and continuous learning: DataOps is a rapidly evolving field, so team members should be adaptable and open to learning new tools, technologies, and best practices.
- Domain knowledge: Depending on your organization's industry and needs, it’s beneficial to hire team members with domain-specific knowledge or experience to understand the unique challenges and requirements of your data operations.
- Experience with Agile and DevOps methodologies: DataOps incorporates elements of Agile and DevOps practices, so some familiarity with these methodologies is a must-have for new team members. They’ll also be interfacing with development teams and will need to speak some of the same language to build great products.
- Cultural fit: Assess whether candidates align with your organization's culture, values, and work style, as this can significantly impact their ability to collaborate effectively and contribute to the team's dynamics.
If you can find team members with most of these skills spread across the roles, you’ll have an advantage that purely technically-focused teams won’t. Before you hire your team, be sure to answer these bigger organizational questions.
Questions to ask yourself when building a DataOps team
You can’t simply hire a group of data engineers, analysts, data scientists, and a data steward and expect them to function seamlessly on their own. They need clear objectives, guidance, ways to measure success, and a healthy budget to thrive.
Before you post job ads, make sure you have answers to these questions:
What is the budget for building and maintaining the DataOps team
Determine the financial resources needed to recruit, train, and retain the team, as well as invest in the required tools and infrastructure.
What are your data goals and objectives?
Define the purpose and desired outcomes of your DataOps initiatives. Understanding these objectives will help you set the right direction for the team.
How will you scale the DataOps team as the organization grows?
Develop a plan for scaling the team as the organization's data needs evolve. This may include hiring additional team members, investing in new tools and technologies, or adapting processes and workflows.
How will the DataOps team interact with other teams?
Does DataOps report to IT leadership? Are their workflows owned by BI? Product? Where do we include them in the software development process? Have a clear org chart and establish open lines of communication and collaboration between the DataOps team and other departments so everyone knows how best to work together.
How will the team ensure data quality and integrity?
Develop strategies and processes to maintain data quality, accuracy, and consistency throughout the data lifecycle. This may include data validation, monitoring, and anomaly detection.
What data security and compliance measures will the team follow?
Ensure that the DataOps team is aware of and adheres to relevant data privacy, security, and compliance regulations. Establish policies and procedures for data protection.
How will the team measure success and track progress?
Define key performance indicators (KPIs) and metrics to evaluate the team's performance and progress toward organizational goals. Regularly review and adjust these KPIs as needed.
What tools and technologies will the team use?
Determine the appropriate tools, platforms, and technologies for the DataOps team, such as data integration tools, big data frameworks, and database systems.
What are the main tools a DataOps team needs to be successful?
A successful DataOps team needs a robust set of tools and technologies to manage the data lifecycle effectively.
While specific toolsets may vary depending on an organization's needs and objectives, the following categories of tools and technologies are commonly used in DataOps:
Data integration and transformation:
- Apache NiFi
- Talend
- Informatica
- Microsoft SQL Server Integration Services (SSIS)
- Fivetran
Data storage and databases:
- SQL databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server)
- NoSQL databases (e.g., MongoDB, Cassandra, Couchbase)
- NewSQL databases (e.g., CockroachDB, Google Spanner)
- Data warehouses (e.g., Snowflake, Amazon Redshift, Google BigQuery)
Big data frameworks and processing:
- Hadoop
- Apache Spark
- Apache Flink
- Apache Kafka
Data quality and validation:
- Great Expectations
- Deequ
- Data Linter
- Data Quality for Talend
Data cataloging and metadata management:
- Apache Atlas
- Data.World
- Alation
- Collibra
Data version control and collaboration:
- Git (for code versioning)
- DVC (Data Version Control)
- Quilt Data
- Delta Lake
Data security and privacy:
- Apache Ranger
- Privacera
- Immuta
- Data masking tools (e.g., Delphix, IBM InfoSphere Optim)
Data monitoring and observability:
- ELK stack (Elasticsearch, Logstash, Kibana)
- Grafana
- Prometheus
- Splunk
Cloud platforms and infrastructure:
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Microsoft Azure
- IBM Cloud
Programming languages and data processing libraries:
- Python (e.g., Pandas, NumPy, Dask)
- R
- Scala
- Java
Data orchestration and workflow management:
- Shipyard
- Apache Airflow
- Prefect
- Luigi
- dbt
The specific tools and technologies a DataOps team needs depend on the organization's data goals, infrastructure, and current capabilities. Choose tools that fit your team's skills and can be easily integrated into your existing data workflows. With the right modern data stack in place, your team can develop your business data capabilities quickly.
Main business benefits of hiring a DataOps team
A DataOps team can deliver significant business value to an organization by enabling efficient, reliable, and scalable data operations.
Here are some of the key benefits of a DataOps team:
- Improved data quality: DataOps teams ensure that data is accurate, consistent, and reliable. High-quality data is crucial for making informed business decisions and driving better analytics and insights.
- Faster time-to-insight: By automating and streamlining data processing, integration, and transformation, DataOps teams significantly reduce the time it takes for data to move from its raw state to usable insights. This enables faster, data-driven decision-making.
- Increased collaboration: DataOps teams help bridge the gap between data producers (IT, data engineers) and data consumers (data scientists, analysts, and business users) by establishing clear processes and communication channels. This fosters collaboration, reduces data silos, and ensures that data is accessible and useful across the organization.
- Enhanced agility and adaptability: DataOps teams leverage Agile methodologies and DevOps principles to ensure that data operations are responsive to changing business needs. This enables organizations to quickly adapt to new data sources, technologies, and market conditions.
- Scalability and flexibility: A well-functioning DataOps team can help organizations scale their data infrastructure and operations to accommodate growth and ever-increasing data volumes. This ensures that organizations can effectively manage their data needs as they evolve over time.
- Risk reduction and compliance: DataOps teams focus on data security and privacy, ensuring that organizations adhere to relevant regulations and industry standards. This helps mitigate potential risks, fines, and reputational damage associated with non-compliance or data breaches.
- Cost savings and resource optimization: By automating data workflows and optimizing data processing and storage, DataOps teams can help organizations reduce costs associated with manual processes, inefficient infrastructure, and data redundancies.
- Innovation and competitiveness: DataOps teams enable organizations to leverage their data assets more effectively, unlocking new opportunities for innovation, customer engagement, and market differentiation. This can provide a competitive edge in data-driven industries.
- Enhanced data culture: A successful DataOps team helps foster a data-driven culture within the organization, where employees at all levels understand the value of data and incorporate it into their decision-making processes.
- Improved return on data investments: By ensuring that data is efficiently processed, managed, and utilized, DataOps teams can help organizations maximize the value derived from their data assets and investments in data infrastructure and tools.
Ready to start building your DataOps team? Here’s how to start.
What next?
Chances are, you already have at least one person responsible for data at your company. If you already have a small team working on data, you’ve begun the process of building a DataOps team even if you call them something else. Now you just have to take steps from those mentioned above and work them into a strategy for hiring the right talent, securing a budget, and growing your DataOps team over time.
One of the biggest elements of success is giving your data professionals the right tools to do their jobs well. Shipyard is a data orchestration platform that makes it easy to launch, monitor, and share data workflows quickly. Sign up for our free Developer Plan. With the free plan, you can build and automate workflows in minutes to start implementing DataOps best practices.
