Analytics engineering: Bridging the data engineer and data analyst gap

Posted by Allan Situma on July 17, 2020 · 5 mins read
With the fast pace of change in data technology, companies are finding themselves hiring for new roles, one of these roles is that of a technical analyst that would bridge the gap between data engineers and analysts

The world of data is still new yet evolving at a faster rate.Besides technology, roles have also been evolving. In the past couple of years, we have seen some roles merge while other new roles emerging. One of the most recent role is the analytics engineer role.This article takes a look at what this new role is and the value it brings to a data team

Agenda

  • Understanding analytics engineering
  • The need for analytics engineers
  • Current technologies for analytics engineering

Understanding Analytics Engineering

Analytics engineering is a specialized field that bridges the gap between data engineering and data analysis. It focuses on transforming raw data into clean, organized, and useful datasets for analysis and business intelligence. Unlike traditional data engineers who focus on building and maintaining the infrastructure for data storage and retrieval, analytics engineers are responsible for shaping data to be directly usable by analysts and business users.

Core Responsibilities of an Analytics Engineer

  • Data Transformation: Creating data models, developing ETL (Extract, Transform, Load) processes, and ensuring data quality.
  • Collaboration: Working closely with data analysts, data scientists, and business stakeholders to understand their needs and ensure the data infrastructure supports those needs.
  • Automation and Optimization: Automating repetitive data tasks and optimizing data pipelines for performance and scalability.
  • Documentation and Governance: Maintaining comprehensive documentation for data processes and ensuring compliance with data governance policies.

The Need for Analytics Engineers

In today's data-driven world, businesses are inundated with vast amounts of data from various sources. This data holds the potential to drive insights and inform strategic decisions, but only if it is properly managed and interpreted. This is where analytics engineers come in. Here are a few reasons why analytics engineers are crucial:

  • Data Usability: They transform raw data into clean, structured formats, making it easier for analysts and business users to derive actionable insights.
  • Efficiency: By automating data processes, they free up analysts to focus on analysis rather than data preparation.
  • Scalability: They build robust data pipelines that can handle increasing volumes and varieties of data, ensuring that the infrastructure scales with business growth.
  • Quality and Consistency: They implement data quality checks and standardize data definitions to ensure consistency and accuracy across the organization.

Current Technologies for Analytics Engineering

Several technologies and tools are pivotal in the work of analytics engineers. These tools aid in data extraction, transformation, loading, and modeling, and they ensure data quality and governance. Some of the key technologies include:

Data Warehousing Solutions

  • Snowflake: Known for its scalability and performance, Snowflake allows for efficient data storage and querying.
  • Google BigQuery: A fully managed data warehouse that offers fast SQL querying and real-time analytics.

ETL Tools

  • Apache Airflow: A platform to programmatically author, schedule, and monitor workflows, enabling complex data pipeline management.
  • Fivetran: Automated data integration that synchronizes data from various sources into a data warehouse.
  • Stitch: A simple, extensible ETL service that integrates various data sources into a single data warehouse.

Data Modeling and Transformation

  • dbt (data build tool): An open-source tool that enables data analysts and engineers to transform data in their warehouses through version-controlled SQL. dbt helps in building modular, reusable data models, testing data quality, and documenting transformations in a version-controlled environment.
  • AWS Glue: A fully managed ETL service that makes it easy to prepare and transform data for analytics. It provides a flexible and scalable way to run data transformations and load data into a data warehouse.
  • Hevo Data: A no-code data pipeline platform that helps in integrating, transforming, and loading data from various sources into data warehouses. Hevo provides real-time data processing and automatic schema mapping to simplify data preparation.

Data Quality and Governance

  • Great Expectations: An open-source framework for validating, documenting, and profiling your data to ensure it meets the desired quality.
  • DataHub: An open-source metadata platform for data discovery, data governance, and collaboration.

Business Intelligence Tools

  • Looker: A data exploration and visualization platform that integrates seamlessly with various data sources.
  • Tableau: Renowned for its powerful visualization capabilities, Tableau helps transform data into actionable insights through interactive dashboards.

Analytics engineering is an evolving field that continues to grow in importance as organizations increasingly rely on data to drive their decision-making processes. By understanding the role, recognizing the need, and leveraging the right technologies, businesses can unlock the full potential of their data assets.

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