Analytics Engineer 101: Job Description, Salary & More
In the world of data, the role of an analytics engineer has emerged as a critical player. Bridging the gap between data engineering and data analysis, analytics engineers are the linchpins that ensure data is not only accessible but also optimized for performance. This article will delve into the role of an analytics engineer, the skills required, and the tools they use. We will also explore how an analytics engineer differs from other roles in the data field, such as data engineers and data analysts.
An analytics engineer plays a pivotal role in managing and transforming data. They are responsible for moving and transforming data from the source so that it can be easily analyzed, visualized, and acted upon by data analysts or business users. They deal with the data itself as well as the moving of data. It is their job to ensure data is ingested, transformed, scheduled, and ready to be used for analytics. Many analytics engineers are the brains behind the “modern data stack”. They decide which tools to use for ETL/ELT and then set them up.
In essence, an analytics engineer is a hybrid role that combines the technical skills of a data engineer with the business acumen of a data analyst. They are involved in strategic data decisions, warehouse architecture, and automating data models. This role requires collaboration across different teams within a company, allowing them to interact with growth and marketing teams while still coding in SQL and Python and expanding their coding skills.
To become an analytics engineer, one must possess a unique blend of technical and business skills. Here are the top skills required for analytics engineering:
- SQL: SQL is the language of data. Analytics engineers use SQL analytics functions every day to write their transformations within data models.
- Experience with dbt: Dbt is the leading data transformation tool in the industry. It is used by analytics engineers to write their data models.
- Python: While analytics engineers don’t need to be master coders, it is still important that they know how to code in Python. A majority of data pipeline tools utilize Python.
- Experience with modern data stack tools: Familiarity with the most popular tools in a modern data stack is crucial. This includes tools like Snowflake, Google Big Query, Fivetran, Matillion, Airbyte, Looker, ThoughtSpot, etc.
- Communication: As an analytics engineer, you need to keep everyone up-to-date on the status of your data. You need to communicate when data quality is compromised, a pipeline is down, and most importantly, to understand what the business needs.
While both roles are integral to a data team, there are distinct differences between an analytics engineer and a data engineer. A data engineer is someone who builds the infrastructure to support the storing and movement of data. They focus on how they can support the data, using Python to write data pipelines, Spark to process data, and cloud technologies like AWS to deploy infrastructure. A data engineer works closely with other developers such as software engineers.
On the other hand, an analytics engineer is more involved in the business side of things. They interact with business teams and data analysts, helping to shape the
team’s priorities. They are not just building infrastructure, but also looking at the data itself, ensuring it is ready for analysis and decision-making.
The salary of an analytics engineer can vary depending on the location, the size of the company, and the individual's experience. However, according to Glassdoor, the median salary for analytics engineers in the United States is $111,665. This figure can be higher in tech hubs like the Bay Area and for large tech companies. It's important to note that salary should not be the only deciding factor when choosing a career path. The role of an analytics engineer is challenging and rewarding, offering plenty of opportunities for growth and learning.
SQL (Structured Query Language) is a critical tool in the arsenal of an analytics engineer. It is used to manage and manipulate databases, and is the standard language for relational database management systems. SQL commands can be used to search the database, create tables, delete data, update data, and perform complex queries, among other tasks.
In the context of analytics engineering, SQL is used to write transformations within data models. This allows the analytics engineer to structure the data in a way that is most useful for analysis. For example, an analytics engineer might use SQL to aggregate data, calculate averages, or filter data based on certain criteria.
Communication is a key skill for an analytics engineer. They need to be able to communicate effectively with both technical and non-technical stakeholders. This includes keeping everyone up-to-date on the status of the data, communicating when data quality is compromised or a pipeline is down, and understanding what the business needs in terms of data analysis.
Good communication skills also help an analytics engineer to work effectively within a team. They need to be able to collaborate with data analysts, data engineers, and other members of the data team, as well as with stakeholders from other parts of the business.
Analytics engineers use a variety of tools to manage and transform data. These tools form part of the 'modern data stack', a set of technologies used to ingest, transform, store, and analyze data. Here are some of the key tools used by analytics engineers:
SQL: As mentioned earlier, SQL is a fundamental tool for analytics engineers. It is used to write transformations within data models and to manage and manipulate databases.
dbt (data build tool): dbt is a command-line tool that enables analytics engineers to transform data in their warehouses more effectively. It allows engineers to write transformations as SQL scripts, and then run these scripts in a specified order, with full dependency tracking.
Python: Python is a versatile programming language that is widely used in data analysis. Analytics engineers use Python to write data pipelines and to automate data processing tasks.
Modern data stack tools: These include data warehousing solutions like Snowflake and Google Big Query, data integration tools like Fivetran and Matillion, and data visualization tools like Looker and RATH (opens in a new tab).
While both roles involve working with data, there are some key differences between an analytics engineer and a business intelligence (BI) engineer. A BI engineer primarily focuses on designing and implementing systems that enable others in the organization to analyze data. They work with BI tools to create dashboards and reports, and they often work closely with business stakeholders to understand their data needs.
On the other hand, an analytics engineer is more involved in the technical aspects of data management. They work on the ingestion, transformation, and preparation of data for analysis. They also often work with SQL and Python, and they may be involved in selecting and implementing the tools used in the data stack.
Becoming an analytics engineer typically requires a background in a field like computer science, data science, or a related field. However, it's not just about having the right degree. Here are some steps to becoming an analytics engineer:
Learn SQL and Python: These are two of the most important tools for an analytics engineer, so having a strong understanding of both is crucial.
Get familiar with the modern data stack: This includes tools for data ingestion, transformation, storage, and analysis.
Develop your communication skills: As an analytics engineer, you'll need to be able to communicate effectively with both technical and non-technical stakeholders.
Gain experience: Whether it's through internships, work experience, or personal projects, getting hands-on experience with data engineering tasks is invaluable.
A data analyst focuses on analyzing data and providing insights to help with decision-making. They typically use tools like SQL and Excel, and they often create reports and dashboards. An analytics engineer, on the other hand, works on the technical aspects of managing and transforming data. They use tools like SQL, Python, and dbt, and they are involved in selecting and implementing the tools used in the data stack.
According to Glassdoor, the median salary for analytics engineers in the United States is $111,665. However, this can vary depending on factors like location, company size, and individual experience.
Key skills for an analytics engineer include SQL, Python, familiarity with the modern data stack, and good communication skills. They also need to understand business terms and be able to work effectively with both technical and non-technical stakeholders.