The document summarizes the career path of Lasse Benninga from data engineer to analytics engineer. As a data engineer, Lasse worked on predictive maintenance projects at KLM and cloud data engineering consulting. While he enjoyed the technical challenges, Lasse disliked being removed from business impact and domain expertise. Analytics engineering has emerged with trends like cheap storage, cloud data warehouses, SQL standardization, ingestion tools, and DBT. Analytics engineers leverage these tools to become subject matter experts who clean, model, and curate datasets for others using best practices like version control, testing, and documentation. The document provides tips for becoming an analytics engineer such as practicing SQL, building end-to-end data projects, and applying for
2. Lasse
Benninga
• Analytics Engineer @ GDD since
October 2021
• Studied Informatics in Groningen
• Love OSS and Data <3
• Live in Utrecht
• Enjoy podcasts, running
3. GODATADRIVEN
Chapters
• My (short) career as a Data
Engineering
• The Rise of the Analytics Engineer
• Differences between DE and AE
• Tips for up-and-coming AE’s
5. My career
timeline
• Studied HBO Informatics in Groningen: Software Engineering (2013 /
2017)
• Followed the BI & Big Data Traineeship at Young Capital (2018)
• Placed as a trainee Data Engineer @ KLM (2018)
• In-house DE for KLM (2019)
• DE consultant (2020-2021)
• Analytics Engineer (2021+)
6. Data Engineer @ KLM
(2018-2020)
• Predictive Maintenance on Boeing fleet
• Worked in a product team of Data Scientists, Data
Engineers,
Aircraft Engineers
Main responsibilities:
• Extracting data from FTP
• Moving data into Hadoop cluster
• Writing Spark (PySpark + Scala)
• Using Containerization (Docker)
• Monitoring data quality
• Creating dashboarding
• More on the pipeline side than the insights side
7. (Cloud) Data Engineer @ Consultancy
(2020-2021)
• Cloud-native Data Engineering
• Tools for provisioning infrastructure like Terraform,
Ansible
• Mainly writing Python, with Servless functions
like AWS Lambda
• Less oriented on the data and the stakeholders
8. Working as a Data
Engineer
Liked
• Learned about a lot of new cool technology:
Spark, Hadoop etc.
• A lot of freedom to investigate solutions, try out
said technologies
• Maintaining working infrastructure is
challenging and rewarding
Disliked
• Quite far removed from the business impact
• “Generic” work that does not require very domain
specific knowledge
• Hard to become an expert on the data itself
when you are battling the system
12. What is an
Analytics
Engineer?
• Term coined in 2018 in the blog Locally
Optimistic
• Bridges the gap between Data
Engineering and Analyst
• Relies heavily on ”the modern data
stack”
• Goes hand-in-hand with the motto of
DBT Labs – Bringing software
engineering best practices to Data
Analysts
14. What is an
Analytics
Engineer?
Trend #2:
Managed
Cloud
Datawarehous
e
https://www.castordoc.com/blog/cloud-data-warehousing-the-past-present-and-future
17. What is an
Analytics
Engineer?
Trend #5:
Move from ETL to
ELT
Architecture
https://validio.io/blog/dbt-and-the-analytics-engineer--whats-the-hype-about
19. dbt
Released in 2016 , data build tool (dbt) is a workflow tooling
that is meant for transforming data inside a data warehouse:
• Open-source software with a SaaS offering (dbt Cloud)
• Connects to most major cloud DWHs
• Runs SQL statements on the DWH
• Creates a DAG of SQL ”models”
• Supports documentation in code
• Cloud offering contains Scheduler, IDE, Documentation hosting
24. Data
Engineer
• More like a traditional Software
Engineer
• Writes in one or more programming
languages
• Leverages a large set of tools,
custom built or off-the-shelve,
manages services and
infrastructure
• Less of an expert on the data itself,
more focused on getting data from
A to B
• Often more expensive hourly due
to niche knowledge
Analogy: Plumber
Analytics
Engineer
• Tends to be more like an analyst,
but with technical chops
• SQL-first, then probably Python
• Leverages a lot of SaaS
• More of a subject-matter/domain
expert, depending on the team
• Curates high-quality datasets
• Often cheaper hourly rate than Data
Engineers
Analogy: Librarian
25. Analytics
Engineer
• AE’s own and clean the datasets, building quality
and performant data models to help other data
team members work save time.
• AE’s also work on the design and development of
CI/CD pipelines so that data teams can efficiently
apply, test, and release analytics.
• AE’s think about the data model and modelling
techniques.
• AE’s Software Engineering best practices like
Version Control, Data Testing, and adding
documentation.
27. Tips for up-and-coming
AE’s
• Try out a Cloud DataWarehouse, ingestion
+ dbt + visualization tool
• Create an end-to-end project combining
these (free) tools
• Add some CICD, data quality testing,
monitoring tooling
• Become proficient in SQL
(hackerrank/leetcode)
• Apply at Xebia Data!