What is Analytics Engineering? Skills, Responsibilities and Trend
By Brian Laleye · July 12, 2022 · 13 min read
Data has always been the backbone of any business but recently thanks to technology, digital transformation and knowledge sharing, we’re now aware of the untapped data trapped in various ways at each step of its cycle:
- Multiple sources: Files, Databases, Data Warehouses, Cloud applications, IoT
- Type of sources: Private, Public, Anonymized
- Different format: Structured, Unstructured
- Generators: Humans, Algorithms, IoT, Robots
- Operability: API, Open Protocols, Vendor-lock protocols, SQL, No SQL
An organization’s data is its most valuable asset but it’s also one that is often overlooked or undervalued.
Data drives innovation, helps solve business problems and creates competitive advantage.
Businesses are realizing that the importance of data isn’t just limited to analytical departments anymore – it touches every department in an organization and informs every decision they make.
Thus, when it comes to analyzing data and getting the most of it without spending tons of money, an emerging role has seen tremendous growth in the past years: Analytics Engineer.
Analytics engineering is the combination of business intelligence, engineering and data analysis to create actionable insights from raw data.
It makes use of various tools to slice and dice data in order to make better decisions based on analytics.
So let’s dive into the hottest role in modern data teams and why it is crucial to rejuvenate the structure of Modern Data Teams.
Summary
What is Analytics Engineering? And Why is it Essential in today’s business environment
Analytics engineers develop methodologies for processing, transforming, testing, versioning, deploying and documenting data pipelines to extract useful information for data teammates (mostly data analysts).
They do apply software engineering methods and processes like version control, testing or CI/CD to Data Analytics.
The Data Team is dead…
Until the early 2010s, organizations were hiring Data Engineers to build the IT infrastructure with an ETL approach.
In short, creation of pipelines to extract data from a SQL database and SaaS applications, transforming data and finally loading it into your warehouse.
Then, a newly hired data analyst would generate insights by building dashboards and reports on top of the data sources with SQL & Excel skills.
This kind of role supposes ever-demanding requests from data consumers to create, modify, adapt pipelines to their specific needs.
The 3 main consequences that have favored the need of a Modern Data Team’s structure are:
- Expensive: Onerous on-premise tools (implementation, onboarding and subscription)
- Unscalable: High dependance on one or two people that concentrate all the knowledge in data pipelines (queries, versioning, documentation)
- Complex infrastructure: Ever-increasing data sources has led to add more and more tools for ingestion, storage, transformation, analysis & visualization
Long live (Modern) Data Team!
Since then, 3 factors have favored the emergence of Modern Data teams:
- Next-gen Cloud-based Data Warehouses (BigQuery, Snowflake, Redshift) enabled data storage and processing to be cheaper and usage-based while increasing performance.
- Shortage of tech skills (i) due to an ever-increasing amount of data generated, consumed and copied every year, (ii) due to an imbalance between supply and demand for software, engineering and analytics projects (in other words, there are more IT/Tech projects worldwide than people able to respond to them). Another factor, there is a high variation span in terms of hard skills for a given role that creates a skill gap internally.
- The No/Low-Code revolution has lowered the entry barrier to ease the adoption of tech & engineering skills and thus filled the gap in terms of IT projects created and achieved. Think of the tremendous value added in this space by Zapier.
Around 2015s, more and more tools have been developed and deployed to ease the process of handling an end-to-end data pipeline (ETL).
A growing number of business users were avid to access self-serve analytics without any structure or deep technical knowledge..
This is why an approach has emerged, at that time, and has been developed: ELT (extract-load-transform), to get the most out of raw data by transforming it in ready-to-consume format for analytics.
Nowadays, the newest approach in terms of analytics is reverse-ETL to activate in any organization operational analytics.
To achieve this, the obstacles on your way are:
- Need of mastering query languages: SQL, NoSQL, Python, Excel..
- Shortage of highly-skilled tech people
- Expensive tech team both internal and external. Multiplication of tools to compensate so the tech stack becomes a mess to manage
This is why RestApp entered the market to be the next-gen Data Activation Platform to transform and model data on top of data sources. Built on Apache Spark and SQL, RestApp enables the data transformation and processing in the data team’s scope, especially Analytics Engineers.
Analytics Engineer’s Role and Responsibilities
Analytics engineering is a multi-disciplinary role and can be defined as the discipline of engineering applied to the practice of analytics and big data.
The Analytics Engineer blends technical expertise with domain knowledge to craft meaningful insights from data and deliver them to users in a timely manner.
This role is more and more central in growing data teams since it helps bridge the gap between technical and business teams and align them to continuously be a data-driven decision making organization.
The analytics engineer role comes with three broad areas of responsibility:
Create & Manage ready-to-use Data Pipelines
Whether it is ETL, ELT or reverse-ETL purposes, the Analytics Engineer is the owner for delivering clean data sets to the end-users (like data scientists, data analysts, data engineers, or other non-technical stakeholders for self-service analytics).
Building data pipelines entails a variety of tasks, such as gathering raw data from various sources, cleaning it up (data transformations), and feeding it into a database or data warehouse so that end users can access it.
Designing data models takes a lot of the analytical engineer’s time in order to improve both the data warehousing (such as query execution speed) and the data consuming elements (e.g. building such a snowflake model to optimize analytical queries).
The procedure combines analytical understanding with architectural engineering design.
Handles Data Operations
Analytical engineers oversee the cycle of data as a product, which includes managing the deployment of data warehouses and the operational facets of data pipelines.
This involves keeping an eye on the system-wide data quality, managing tasks, testing, automating, CI/CD, managing versions and validating metrics and other associated consequences of data pipelines.
Work hand-in-hand with Business Users
The role entails participating in generating and maintaining overtime the documentation and the business semantics to align all the stakeholders whether technical or not with pipeline description, setting data alerts and sharing knowledge internally.
With RestApp, be your team’s data hero
by activating insights from raw data sources.
Differences with Data Analytics, Data Engineering, Data Science?
Analytics engineers provide ready-to-use data sets or data-as-a-product that are well-described, tested, documented, and code-reviewed.
Because of the excellent quality of this data and the accompanying documentation, business users may utilize BI tools to conduct their own analysis and receive accurate, consistent results.
This is why this new role is a related but distinct role from Data Engineering.
The focus of Data Engineering is all about managing the data platform and making it available as reliably, securely and fast as possible.
Analytics Engineering is the counterpart, responsible for building analytics applications or plugins on top of that platform to provide real value to customers.
Here’s what we think the differences between these roles:
The scopes implied by these roles are porous but this is for the better since this is a new way of approaching analytics so the label and attributes will evolve in theory and in practice.
Another way to spot if this role is partially filled internally or you need one, is by having those questions:
- How can we optimize this pipeline to answer more business questions?
- How can we help Data Analysts understand on their own this table or data pipeline?
- How best can we anticipate broken charts in Looker and alert relevant stakeholders?
What is the skillset in an Analytics Engineering Role?
The scope of skills may vary across organizations and industries but here’s the list of key attributes associated with an Analytics Engineer role:
- Data as the epicenter of day-to-day operations: willingness in solving data-related problems has to be a true guide to follow this path, one way could be one data engineer willing to extend business knowledge or data analytics people willing to get more techy.
- Master SQL/Python: this is prohibitive if not proficient in querying data sources. The role involves logical mindset for data modeling, optimizing queries and building data pipelines.
- Tech best practices adopted: analytics engineers replicate software methodologies to ensure all analytics processes run smoothly.
- Interpersonal relationship and communication abilities: being at the crossroads of engineering, analytics and business, it implies responsibilities to share and communicate knowledge the best way possible with teammates and ensure consistent alignment. Written communication is also crucial to document any relevant key learnings in the data team.
- Harnessing the art of collaboration: sharing data sets and working in building data sets for any department are not the same, it is essential for Analytics Engineers to be comfortable in dealing with technical and non-technical people and working with different levels of seniority.
What Toolkit Analytics Engineers deliver value with?
The role of analytics engineering is becoming prominent in the modern data stack, we could even label it the Analytics Engineers’ Data Stack:
The use and responsibilities of these tools may vary across organizations but Analytics Engineers are the ones mastering and owning the data transformation layer for the organization.
The data transformation part is fundamental to be a data-driven decision making business by delivering data-as-a-product to data-consumers with embed data quality rules:
- removing inaccurate, null or corrupted data entries
- filtering to remove irrelevant, duplicated, or confidential data
- joining several tables by their matching keys (id, email…)
- splitting a column into multiple ones and the list goes on
A lot of attention is made on the tools needed as this role evolved in data teams but we can also dwell on the mindset required to deliver value for teammates.
The insatiable curiosity, problem solving approach and interpersonal aptitude are as essential as the hard skills to succeed in this role.
Why do you need an Analytics Engineer in your team?
Most companies believe they don’t need a dedicated analytics engineer.
That myth has been dispelled though, as the volume of data that needs to be analyzed is growing at a rapid pace.
The increasing volume of data in most organizations requires more people with expertise in using Big Data technologies to analyze it and gain insights that provide value for business decisions.
No doubt, building analytics solutions that can adapt to changes in your business environment and evolve as required will be an asset for any organization.
It’s not rare that companies find themselves with a lot of data but without the ability to mine it or turn it into actionable insights.
There are a multitude of reasons to hire an analytics engineer, the most important being business benefits.
You’ll have access to a deeper understanding of your customers and their behaviors, habits, and motivations but also challenge your internal process to deliver value to them.
With this knowledge in hand, you can produce better products and services that generate more revenue and also reduce unnecessary IT/Software costs that could be only unveiled by someone looking at it!
Analytics Engineers are rising to free up data analysts and engineers to focus on critical business choices and to be more creative and skilled in designing data pipelines.
As operational teams upskill themselves in their use of analytics, Analytics Engineers are taking a central role in infusing engineering practices applied to data in the rest of the organization.
They’re contributing to the democratization of data analytics across teams and departments that were until recently siloed and separated.
Career, Trends and Perspectives
According to Linkedin, there are more than 500k professionals globally with a “Analytics Engineer” title and its variant “Data analyst engineer” for instance.
The role is growing exponentially given the gap between demand for data professionals and supply and since analytic engineers possess engineering’s best practices, analytics skills and business sense, they can apply in almost every job that has data, analytics or engineer in it:
- Data Analytics Engineer
- Data Analytics Developer
- BI Engineer
- Modeling & Data Analytics Engineer…
Whether at a small or large company, a tech/data profile is needed to take in charge all the data pipelines to ensure the organization is a data-driven one
Any company lies on data and requires solving data tasks like collection, cleaning, transformation to get insights and move the business forward.
For the past few years, No Code and Low Code solutions have been taking over the market.
With the advancements in software development and web technologies, it’s easy to build powerful applications without having to write a single line of code.
In fact, No Code and Low Code platforms have become so mainstream that they’re now helping companies around the globe embrace digital transformation with speed, agility and affordability.
The digital transformation of businesses is becoming more and more essential in today’s world, thanks to these technologies, an average organization can get hold of their data needs easily and quickly with low or even any programming skills and be truly data-driven without spending a ton of money.
This role is the best of both worlds (data and engineering) in a context of a modern data stack that is evolving towards No/Low Code solutions to help businesses in their data journey.
Small and medium organizations can’t afford plethoric data teams so Analytics Engineers have an opportunity to develop skills in a multidisciplinary field.
If you’re interested in starting with connecting all your favorite tools, check out the RestApp website or book a demo.
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Additional resources:
- What is Data Transformation? Definition, process and the new generation of tools
- [Series] How to Build Data Pipelines in No-Code? A 6-Step guide with examples
- What is Reverse ETL? (and why apply it to your business?)
- What is Operational Analytics? (and why is it essential to your business?)
- What is Data mesh? The new data architecture
