How I became an Analytics Engineer

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The Analytics Engineer: A Comprehensive Introduction

Welcome, everyone, to a space designed to give clarity about a relatively new role in the data team – The Analytics Engineer. This title is fairly young, and there are limited resources available online. Therefore, in this conference session, we aim to explain the role of an Analytics Engineer, explore its significance in the business, and discuss how you too can pave your way to this ever-evolving role.

Understanding the Role of an Analytics Engineer

Several years ago, Spotify recognized a missing piece in their team structure. They needed an individual who could bridge the gap between understanding the business, software engineering practices, and technical know-how. Essentially, they required someone who lived in the intersection of deep business knowledge and robust technical skills. In addressing this need, they inadvertently birthed the role of an Analytics Engineer.

We are looking for an expert in building out analytical data layers to join the band and help drive a data-first culture across the ad supported business in Spotify.

The Analytics Engineer's role exists 'in-between' a Data Engineer and a Data Analyst. The Data Engineer's role involves preparing and maintaining the data infrastructure, while the Data Analyst uses this data to solve problems and answer business questions. The Analytics Engineer bridges the gap between these two roles, introducing good software engineering practices to the analysts' efforts and bringing a business mindset to the Data Engineering tasks.

Data Roles – A Closer Look

Data Engineer

These individuals focus on more technical aspects of data management. They handle data integrations, manage data pipelines and endpoints, and maintain the data warehouse.

Data Analyst

On the other hand, Data Analysts deep dive into business-oriented tasks. They answer business-related questions, work with business stakeholders, build dashboards, do forecasting, and handle reporting.

Analytics Engineer

This 'in-between' role ensures clean, transformed data is provided for analysis. Applying the best software engineering practices to data-related tasks, maintaining documentation and definitions, and training business users on using data tools are some of the essential responsibilities of an Analytics Engineer.

Tools of the Trade

Given the bridge-like nature of the role, Analytics Engineers utilize a spectrum of tools. Some of these include:

  • Cloud data tools like Snowflake, BigQuery, and Redshift for data storage,
  • ETL tools such as FiveTran, Talend, and Stitch for data loading,
  • DBT or Dataform for Data modelling, and
  • BI tools like Looker, Mode, and Tableau for data presentation.

The Journey to Becoming an Analytics Engineer

The role of an Analytics Engineer requires a unique blend of business acumen, technical prowess, and data management knowledge. This combination doesn't come overnight – it's a career journey, refined and tailored over time. Below is the career trajectory for an Analytics Engineer:

  1. Start with a strong understanding of databases concepts, SQL, and data pipelines, alongside insights into reporting and software engineering best practices.
  2. Experience in data warehousing, ETL development, and working with an ERP system gives valuable practical experience.
  3. Branch out into being an IT systems specialist or SQL developer to deepen the technical knowledge combined with business understanding.
  4. Finally, merging all the skills leads to the role of the Analytics Engineer, a perfect blend of technical and business knowledge.

In Conclusion

The Analytics Engineer is a career path with vast opportunities and immense potential. For those interested to pursue this path, focus on continually expanding your knowledge around databases, SQL, data pipelines, reporting, and software engineering best practices. With the advancing technology and growing businesses need to make data-driven decisions, the role of an Analytics Engineer will continue to become increasingly significant.

If this piece sparked your interest and you want to dive deeper into the topic, feel free to reach out or find additional resources in the GitHub repository linked here.


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