The role of an analytics engineer is pivotal, as they occupy a critical spot on data teams.
This article unfolds the multifaceted world of analytics engineering, shedding light on their role within organizations, how analytics engineers are similar and different from data engineers and data analysts, and how the role itself is evolving.
What is analytics engineering?
Analytics engineering bridges the gap between data engineering and data analysis. Those working in this valuable role on data teams curate and prepare data to make it more accessible, reliable, and useful. In doing so, analytics engineers ensure that data analysts and business intelligence (BI) professionals have the raw materials needed to do their jobs.
“Make the data better,” sounds simple enough. Let us not forget, though, that quintillions of bytes of new data are now being created each day. Data sources, schema, and formats are legion. And it’s the analytics engineers who are the hook for imposing order on all this binary chaos. Fortunately, they have some very effective tools at their disposal.
But before touching on the tools analytics engineers have at their disposal, let’s make sure we clearly define the role, especially in contrast to data engineering and data analytics, as there are natural overlaps between the three.
Analytics engineers vs. data engineering vs. data analytics
Don’t let the “vs.” fool you. Analytics engineering is a very collaborative role. It has to be, as it serves as a conduit between data engineers and analysts. Any collaborative role will tend to overlap with those congruent to it within an organization. So, for clarity, let’s examine what makes these roles different.
Data engineers build the ecosystems that enable the data to flow. Increasingly, businesses rely on robust, efficient, scalable data pipelines, architectures, and storage solutions. The data engineers make this happen and, as a result, raw data can be ingested from various sources, stored, and made available for further processing.
Those in an analytics engineering role certainly deal with data infrastructure in their day-to-day. However, more so than data engineers, their efforts focus much more on data’s end-users. These users are often those in BI and analytics, as mentioned above. But business leaders and stakeholders also increasingly require high-quality data to inform decision-making and digital transformation initiatives.
As their title suggests, analytics engineers may interact heavily with data to help support business strategy, working with data visualization and BI tools to make said insights clear and accessible. This means anyone in analytics engineering must have a strong understanding of the business domain.
Data analysts regularly dig far deeper, though, in order to shed light on more elusive insights, build reports and dashboards, and perform ongoing statistical analysis. And, due to their complementary knowledge, it’s the analytics engineers that analysts often lean on to help prepare and structure data once they’ve resurfaced with the goods.
In essence, then, analytics engineering serves as a bridge, connecting the more technical, infrastructure-focused world of the data engineer to the more business-focused, analytical world of the data analyst. It’s a pivotal role within a world of pivotal roles. Knowing this is key, as it provides valuable context for understanding what responsibilities being a data team’s bridge entails.
Common responsibilities of the analytics engineer
As a bridge, analytics engineers play a key role in the ability of a given team to shape data infrastructure and processes within an organization. Here are the common responsibilities that, when enacted together, help that all shake out.
Data modeling and transformation
As mentioned above, analytics engineers design and implement models to make data ingested by their organization accessible and useful. This involves understanding when data normalizing or denormalizing is appropriate, as well as creating and maintaining transformation jobs that convert raw data into analytics-ready forms.
Analytics engineers will also collaborate with data engineers to make sure the data ingestion process itself happens in an efficient and timely manner. Doing so may involve performance monitoring of data warehouses and designing specific schemas and tables that improve querying efficiency.
ETL processes and data quality
Analytics engineers also design, build, and maintain ETL processes—a favorite of ours here at Shipyard. This is important, as it ensures data within the organization gets reliably transferred from various sources to all data stores.
This role is also one on a data team responsible for implementing tests and monitors to ensure high data quality (i.e., data that is highly reliable and accurate). This involves setting up anomaly detection, data validation checks, and alerting systems to keep tabs on overall data integrity.
Unfortunately, collaboration does not tend to happen on its own. This is why analytics engineers work closely with their analyst peers to understand specific data requirements, and that they have access to the data they need. They may assist data scientists in the organization by cleaning datasets for model training and validation.
Analytics engineers may also play a formative role in communicating with business stakeholders, ensuring the data team understands operative business requirements, and that the data they help curate remains aligned with business goals.
Data teams—and DataOps more generally—often succeed or fail based on the quality and consistency of their internal documentation. In this sense, analytics engineers are often the unsung heroes documenting transformation logic, data logic, and overall data flow in the organization.
This documentation benefits the entire organization, as clear concise information helps other teams understand and more fully utilize available data structures.
Tech selection and optimization
Keeping up with the latest technologies, best practices, and tools is a responsibility unto itself. But it’s essential, as the nature of their role means analytics engineers often are well-suited to recommending new tools or platforms that can benefit data infrastructure and performance.
To that end, these individuals will also have a hand in performance optimization, fine-tuning SQL queries, data processing tasks, or database schemas. They may also be tasked with helping ensure analytics queries and dashboards run efficiently, without overloading supporting systems.
Compliance and data governance
The ability of an organization to be in compliance and effectively govern its data is increasingly mission-critical for businesses. Thus, the analytics engineer may often work with legal, security, and compliance teams to help ensure data is handled, processed, and stored according to specific regulations and best practices.
In some industries, this responsibility may also involve implementing security measures and access controls to safeguard sensitive or personal data.
Training and continuous learning
Finally, this is a data role in which it pays to stay updated with the evolution and advancements in analytics tools, ETL processes, and data management. Doing so may involve participating in workshops and training so the analytics engineer is constantly upskilling, and able to bring new tools and methodologies to the organization.
It’s a responsibility that reinforces the reality that the average analytics engineer wears many hats, from being a tech expert in data processing to functioning as an expert communicator, one able to become the bridge needed to connect raw data with actionable insights.
7 essential types of analytics engineering tools
Granted, no two data teams operate the same way. And as technology and best practices continue to evolve, types of tools will come and go. But today’s analytics engineers can, more or less, expect to use the following types of tools to enable the following capabilities:
1. Data transformation
Some analytics engineers will opt for ETL capabilities like Shipyard's, which are hyper-scalable and bundled with large libraries of helpful integrations. Others may opt for tools to handle specific needs in the role, like one tool for data extraction and loading and others for transformation, integration, and data flow facilitation.
Regardless of the choice to leverage multiple tools or one transformation tool to rule them all, managing a modern data stack requires the efficiency, scalability, and data quality features these tools provide.
Examples: Shipyard, Stich, Talend, Apache Nifi
2. Data warehousing
Each year, more and more businesses are making good on plans to move and store their data in the cloud. Data warehousing tools help the analytics engineer consolidate data from various sources into a single repository.
Proper use of these tools can also increase performance by enabling faster queries and help keep data accessible to users throughout the organization.
Examples: Snowflake, Google BigQuery, Redshift
3. Data modeling
Data modeling tools help analytics engineers understand data relationships and, just as importantly, facilitate that understanding throughout the organization. Modeling tools also aid in developing and optimizing database schemas and can further improve the efficiency and accuracy of data retrieval.
Examples: LookML, Data Built Tool (dbt)
4. Data visualization and business intelligence
While also used by data analysts and those in BI, data viz tools make it easier for analytics engineers to simplify complex data comprehension through visuals. This simplification also has other benefits, making data exploration easier and facilitating more and better data-driven decision-making.
Examples: Looker, Power BI, Tableau
5. Data version control
Analytics engineers also need tools to assist with data traceability so data is tracked and audited when changes are made. Traceable data makes it possible to ensure processes and models can be consistently reproduced. The controlled data changes these types of tools provide also ease collaborative friction, key for AE’s being able to maximize their impact.
Example: Data Version Control (DVC)
6. Workflow management
Automation is essential for high-performing data and DataOps teams. Workflow management tools enable the automation of an organization’s analytical pipelines and data workflows. These tools also often facilitate data-process monitoring and can make the processes themselves easier to optimize.
Examples: Shipyard, Prefect, Apache Airflow
7. Programming languages
Arguably one of the most essential tools in the analytics engineer’s arsenal, programming languages allow data manipulation and analysis processes to be tailored. They’re key for automating data processing tasks. The use of programming languages also enables extensibility, helping tools and platforms function together productively.
Examples: Python, R, SQL
What the future holds: How the role of analytics engineer may evolve (and how you can keep up)
Analytics engineering is evolving, driven by technological advancements, organizational needs, and the growing importance of data in decision-making across industries. While the trajectory isn't fixed, several trends and potential directions for analytics engineering can be identified.
Analytics engineers can expect more strategic involvement and cross-functional collaboration as data-driven insights (and projections) become more important to businesses.
Utilizing more machine learning and advanced analytics in the role is almost inevitable as artificial intelligence (AI) capabilities become cheaper, accelerating their promulgation into day-to-day business operations.
We can also expect the ongoing decentralization of data to increasingly affect the role of analytics engineering as well. As data democratization increases accessibility for non-technical users, the role of analytics engineer will be on the front lines, enabling and managing self-service analytics platforms.Yes, we agree. It’s all pretty exciting stuff. And if this article left you as excited as we were in writing it, take a moment to sign up for our analytics and engineering-friendly weekly newsletter.