Introduction
The composition of the data team can vary depending on the size and needs of an organization. However, some common roles are typically included in a data team. These roles include data engineers, data analysts, data scientists, data architects and data administrators, etc. They work to collect, store, analyze, and interpret data to drive business decisions and improve organizational performance. In this blog, I’ll focus on data engineers, and talk about their role and how they collaborate with others.
Data engineer’s key responsibilities
Data engineers are responsible for designing, building, testing, and maintaining the infrastructure that supports an organization’s data needs. Their role is critical in ensuring that data is properly collected, stored, and made available to data scientists and other users in a timely and efficient manner. Some of the key responsibilities of data engineers include:
- Data collection: Data engineers are responsible for designing and implementing systems that collect data from various sources. They ensure that the data is collected consistently and reliably.
- Data storage: Once the data is collected, data engineers must store it in a way that is secure, scalable, and easily accessible. They must select appropriate database technology and design the schema that defines how the data is organized.
- Data transformation: Raw data is often messy and inconsistent. Data engineers are responsible for cleaning and transforming the data so that it is ready for analysis. This includes removing duplicates, correcting errors, and transforming the data into a format that is suitable for analysis.
- Data pipelines: Data engineers design and build the systems that move data from its source to its destination. They create data pipelines that move data from its source to a data warehouse, where it can be analyzed by data scientists.
- Data quality: Data engineers are responsible for ensuring that the data used by data scientists is accurate and reliable. They implement quality control measures and monitor the data to identify any issues.
- Infrastructure maintenance: Data engineers must ensure that the infrastructure supporting data collection, storage, and analysis is maintained and updated as needed. This includes monitoring system performance and making upgrades to hardware and software as needed.
Collaboration with data scientists/data analysts
When data engineers design the data-storage structure, they need to communicate with data scientists and data analysts to well understand their needs, pain points and use cases. They ensure that the required data is accessible and available in the appropriate formats. Raw data is often not immediately ready for analysis, after understanding that, data engineers start to clean and transform the data, ensuring that it is in a suitable format for analysis, like defining which tables need to create, with what columns, the data type of each column, do they need to do some data transformation and aggregation, create the data pipeline and define its running frequency. Moreover, they also need to create an automatic process for data quality checking, which is to verify the received data format, data volume, data value consistency, business-related logic, etc.
Moreover, they work together to establish data quality standards, data validation processes, and data monitoring mechanisms. Data engineers help data scientists and data analysts understand the data lineage, metadata, and data quality issues, ensuring that they can rely on the accuracy and integrity of the data for their analyses. After all these, data engineers gather feedback on data availability, performance, and infrastructure requirements from the data science and analysis processes. Data engineers continuously improve the data infrastructure and systems based on this feedback, ensuring that they meet the evolving needs of the data science and analysis work.
Collaboration with product owners
Before designing new products or new product features, we need to support them from the data side, which means ensuring that data infrastructure and systems align with the goals and requirements of the product or project.
Data engineers work closely with product owners to understand the data requirements for a specific product or project and gather information about the data sources, data formats, data volume, data quality standards and validation rules to meet the product objectives.
Furthermore, product owners often have specific data needs for their products. Data engineers work with them to define the data pipelines that capture, process, and transform the required data. They collaborate on the data flow, transformations, and integration points to ensure that the data is collected and processed correctly. Throughout the development and deployment lifecycle, data engineers collaborate with product owners to gather feedback, identify areas for improvement, and make necessary adjustments to the data infrastructure. They work together to iterate on the data pipelines and systems, aligning them with evolving product requirements.
Collaboration with clients
As a data engineer in a SaaS(Software as a Service) company, data come from the client side. Thus, communicating with clients on data integration is one of my missions. We engage in discussions and meetings to gather information about the types of data the clients need, the sources of data, and the specific use cases or analytics they want to perform. This helps data engineers gain a clear understanding of the client’s data needs. They also work together to determine which data systems, databases, APIs, or external sources are necessary to collect the required data.
Based on the client’s data requirements, data engineers design data integration processes and pipelines. They collaborate with clients to define the data flow, transformations, and any data cleaning or enrichment steps that may be required. Data engineers work with clients to ensure that the data pipelines align with their specific needs and can provide the desired outputs.
Besides, data engineers need to maintain regular communication with clients throughout the project lifecycle. They provide progress updates, discuss any challenges or issues encountered, and seek client feedback to ensure that the data engineering processes align with the client’s expectations and requirements. This helps maintain transparency and fosters effective collaboration.
Conclusion
Data engineer plays a very important role in terms of the data side and business side by providing the infrastructure necessary to support data-driven decision-making. Without effective data engineering, organizations would struggle to make sense of the vast amounts of data they collect and would miss out on the valuable insights that data can provide.
Collaboration among data engineers, data scientists, and data analysts is crucial for successful data-driven initiatives, by working together, they ensure that data is accessible, reliable, and ready for analysis, enabling meaningful insights and valuable outcomes from the data; collaboration with product owners ensure that the data infrastructure supports the product’s needs, enabling data-driven decision-making and delivering value to end-users; by actively engaging with clients, data engineers can design and implement data solutions that meet their specific needs, leading to successful data-driven outcomes.
References
- the blowup, “white and black industrial machine”, unsplash.com. [Online]. Available: https://unsplash.com/photos/qafbp6O0Rr0