Why Data Engineers Run RevOps
A data engineer is like a builder for a company's data infrastructure.
They design, construct, and maintain the pipelines and systems that collect, process, store, and distribute data. This data is essential for various purposes, such as analytics, machine learning, and business intelligence..
…and increasingly: revenue operations.
The roots of data engineering can be traced back to the emergence of databases and data warehousing in the 1970s and 1980s. However, the role of a dedicated data engineer didn't truly solidify until the mid-2010s, with the rise of big data and the need to handle massive volumes of information.
Data engineers add value by harnessing the power of data, and putting it into positions (and states) to help companies grow efficiently.
Improved Decision-Making: By providing clean, reliable, and accessible data, data engineers empower businesses to make informed decisions based on facts and insights.
Increased Efficiency: Automating data processes and optimizing data infrastructure leads to faster and more efficient operations, reducing costs and saving time.
Enhanced Customer Experience: Data engineers enable the creation of personalized customer experiences by providing the necessary data for understanding customer behavior and preferences.
Innovation and New Opportunities: Access to well-organized data allows companies to identify new trends, develop innovative products and services, and explore new business opportunities.
Competitive Advantage: In today's data-driven world, companies that can effectively leverage their data have a significant competitive advantage. Data engineers play a crucial role in making this possible.
Data engineers excel at building and maintaining robust data pipelines, ensuring that RevOps teams have access to clean, reliable, and timely data from various sources (CRM, marketing automation, sales tools, etc.). This foundation is essential for accurate reporting, analysis, and decision-making in RevOps.
In fact, a data engineer is your best bet to actually build your revenue engine - they are experts who can design data infrastructure with scalability and performance in mind. As RevOps teams and data volumes grow, data engineers can ensure that the data infrastructure can handle the increased demands without compromising performance.
Data engineers can design and implement data warehouses and data models optimized for RevOps analytics. This allows for efficient storage, retrieval, and analysis of large datasets, enabling RevOps teams to gain deeper insights into revenue drivers and performance. Their skills in Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes ensure that data from disparate sources is properly integrated and transformed into a usable format for RevOps analysis and reporting.
Data engineers can automate data integration, cleansing, and transformation tasks, freeing up RevOps teams from manual data manipulation and allowing them to focus on strategic initiatives.
Data engineers prioritize data quality and implement data governance practices. This ensures that RevOps teams can trust the accuracy and consistency of their data, leading to more reliable insights and better decision-making.
Data Engineers in Revenue Operations
Data engineers are essential for modern businesses to thrive. Their work ensures that data is transformed into a valuable asset, driving better decision-making, efficiency, and innovation.
Let’s discuss specifically some of the capabilities a strong data engineer can bring to your company’s RevOps Dept.
Data Silo Destruction & Data Unification
Challenge: Siloed data across different business applications hinders a holistic view of customer interactions and revenue performance.
Solution: Use Python and its libraries (e.g., requests, APIs for other platforms) to integrate Salesforce with other systems like marketing automation platforms (Marketo, HubSpot), customer support tools (Zendesk), or financial systems (NetSuite).
In the code below we take this a step further. In this script we fetch related Account and Contact data along with Opportunity information for a more complete view. We also include an example of how to format address data before sending it to other systems. Let’s look at it further then finish unpacking it.