Bringing modern analytical principles to data teams
The recent developments in technology, the rise of the modern data stack, and the prevalence of self-service analytics have disrupted the field of data and analytics. Out of these game-changing events, a new professional role has emerged in the data field: the analytics engineer.
Leverage cloud technologies
The shift from ETL to ELT has decoupled data transformation from ingestion, which changes analytics workflows radically. It allows analysts with both domain knowledge and technical skills to work in the data warehouse directly to transform raw data into report-ready datasets, enabling self-service analytics across the company. This is where the analytics engineer comes in.
Why Organizations Fail to Turn Data Assets Into Business Value
Discover and realize the value of your data assets with the analytics engineer and the modern data stack
Lower technical barriers and scale the development of data products
As companies strive to become more data-driven, the demand for information grows, potentially overloading IT, BI, or data engineering teams and creating a bottleneck.
By leveraging modern cloud technologies, tooling, and analytical principles, analytics engineers can lower technical barriers and empower data analysts and business domain experts to produce high-quality, trusted data themselves.
By bringing data and business expertise close to each other, our analytics engineer help you scale the development of your data products, while lowering your dependency on engineering capacity that can withhold you from realizing value from your data.
The modern data team
Analytics Engineers as a bridge between Data Engineers and Data Analyst
Organizations hire data analysts or train domain experts as analysts to extract meaning from their data and hire data engineers to make the data readily available. However, the collaboration between these two types of experts spawns a new set of challenges, especially when data engineers and data analysts are working in separate silos. Analytics engineering bridges data engineering and data analysis by bringing engineering best practices towards analytical workflows.
Data Engineers
Focus on building a solid foundation for the data infrastructure that can be used by all domains within your organization. In this way, data engineers enable others to build solutions for their own, while providing tools that fit their experience and needs.
Analytics Engineers
Improve the accuracy, reliability, security and speed of delivery of analytics workflows. Speed up the organization-wide adoption of self-service analytics. Start a sane, agile data governance that focuses on quality, value, and usage of data.
Data Analysts
Combine in-depth business knowledge and data skills to support decision making in your organization with reports and visualisations. For that they need to be able to work with aggregated data of enough quality and have the tools to continue to combine, aggregate and improve.
Moving from Data Analyst to Analytics Engineer
The move towards cloud data warehouses and the emergence of new ELT tooling have changed the way we approach data processing. Organizations are moving towards modern data stacks which require new skills. As a result, the roles and responsibilities that existed in traditional data teams are also changing.
Analytics engineers were born as a response to these changes. However, the large and sudden demand for this new role creates scarcity, buzz, and confusion.
Wondering if analytics engineering is for you? Not sure if you have the right skillset? With so many tools in the market, it can seem overwhelming. However, the truth is that everyone in the field is learning. And as a data analyst, you may already have the core skills that will allow you to develop proficiency quickly as an analytics engineer
Why Organizations Fail to Turn Data Assets Into Business Value
Discover and realize the value of your data assets with the analytics engineer and the modern data stack
Managing data at scale
Before data can be transformed into information and, ultimately, business value, there are many elements that need to be in place. A robust data platform and data pipelines are important enablers. In addition, when your organization and analytical capabilities grow, managing your data assets can become challenging as well.
- Data pipelines may break and where and why may not be clear
- Dependencies between datasets (data lineage) is not tracked
- Data source schemas are evolving over time
- Are transformations generating the expected output?
- Sources of errors and data quality issues are not immediately apparent
Our Analytics Engineers leverage tooling from the Modern Data Stack to make sure data assets are managed correctly as the demand for information grows. Tools from the Modern Data Stack aim to lower technical barriers to avoid engineering bottlenecks, which improves time-to-market of new data products.
Analytics Engineering
Blog posts from our Analytics Engineers
- Analytics Engineering
- data
- Data Platforms
- Analytics Engineering
- Data Platforms
- Analytics Engineering
- Data Platforms
- Analytics Engineering
- Data Democratization
- Analytics Engineering
- Data Engineering
- Data Governance
- Data Platforms
- dbt
- Python
- Analytics Engineering
- Data Engineering
- Open Source
Join our Meetup Group Analytics Engineering
If you are interested in learning more about Analytics Engineering and want to stay up-to-date with the latest and greatest in the field, join our Analytics Engineering meetup group! This group is for analytics engineers, data scientists, BI professionals, data engineers, analysts and literally everybody else interested in data democratization, analytics, and how the data landscape is evolving.
Work as an Analytics Engineer?
Is your passion is data and analytics? Do you want to create value at our clients by uncovering, organizing, and making sense of data by using the tools and technologies from the Modern Data Stack? Do you want to help our clients to accelerate data and information availability and facilitate self-service analytics across organizations? Then we are looking for you!
Case Studies
“What I like about working with GoDataDriven, is that I don’t have to worry about evaluating people’s skillset. I know you have already done that. They went through the process, and you made sure that everything is of high quality. Knowing you can depend on that is very reassuring to me.”
“Our customers are, in general, technologically advanced companies that recognize the advantages of having a cloud platform. They understand it doesn’t compromise security but, on the contrary, increases it and allows a solution to be accessible everywhere without complex and expensive on-premises infrastructures.”
“I’m curious to see what our data-driven future will look like. By using data, we now know how to stay relevant for individual visitors. I’d like to go beyond predicting theater occupancy and introduce smart applications to optimize the number of visitors. But that’s not all. Data also helps us better understand the member life cycle and retain members.” – Rick Stammes, Business Analyst at Pathé.
Are you interested in Analytics Engineering?
Contact Bram Ochsendorf (lead data scientist at GoDataDriven) to learn more about analytics engineering, and how it can help your realize value from your data.