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My Path From Data Engineer to Analytics Engineer
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
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My Path From Data Engineer to Analytics Engineer
- 1. Lasse Benninga 15-02-23 GoDataFest My PathFrom Data Engineer to Analytics Engineer
- 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
- 4. GODATADRIVEN •My (short) careeras a Data Engineering
- 5. My career timeline • StudiedHBO 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 aData 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
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- 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
- 13. What is an Analytics Engineer? https://ourworldindata.org/grapher/historical-cost-of-computer-memory-and-storage?country=~OWID_WRL Trend#1: Storage has become incredibly cheap over the past years
- 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
- 15. What is an Analytics Engineer? Trend#3: SQL as the Lingua Franca of Data Transformations
- 16. What is an Analytics Engineer? Trend#4: Off-the-shelve Ingestion Tooling with 100’s of Connectors Fivetran connectors
- 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
- 18. What is an Analytics Engineer? Trend#6: DBT as the central part of the Modern Data Stack
- 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
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- 23. DE vs AE Source:DBT Labs
- 24. Data Engineer • More likea 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 ownand 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.
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- 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!
- 28. Tips for up-and-coming AE’s •Apply at Xebia Data (formerly known as GoDataDriven) as an Analytics Engineer!
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