Analytics Engineering

Analytics Engineer

Analytics engineering at the MoJ bridges the gap between data engineers and data analysts by ensuring that high-quality, well-structured data is available for analysis and reporting.
We focus on transforming raw data into organised, usable formats through data modelling, testing, and automation, enabling analysts and decision-makers to derive actionable insights efficiently.

Our work ensures the seamless flow of data, from its raw state to dashboards and reports that inform policy and decision-making across the department.

When you join you’ll be given access to our operational onboarding Trello for Data and Analytics Engineers. It’s a structured process that helps new hires transition smoothly into their roles during their first six months.

Articles

What Is An Analytics Engineer? Everything You Need to Know

Here are links to free resources to get started in Analytics Engineering by The Kimball Group and DataCamp. DataCamp can help you upskill with a range of core technical skills listed below.

Data Warehouse and Business Intelligence Resources by The Kimball Group

Ralph Kimball's Dimensional Modeling: The Kimball methodology is a widely adopted approach that focuses on business processes and user experience. It emphasizes designing data models that are intuitive for end-users, using techniques like star schemas and snowflake schemas.

Introduction to dbt

dbt, also known as the data build tool, is designed to simplify the management of data warehouses and transform the data within.

SQL Fundamentals

Master the SQL fundamentals needed for business, learn how to write SQL queries, and start analyzing your data using this powerful language.

Core Technical Skills for an Analytics Engineer at the MoJ

Languages

  • SQL: Essential for querying and transforming data, SQL is used extensively to create clean, well-structured datasets.
  • Python / R: Python and R are used for data manipulation, automating workflows, and running more advanced analytics tasks, particularly in R-Studio.

Creating the Data Model Brief and Blueprint

  • Kimball Resources: Proficiency in Kimball’s dimensional modelling concepts to design robust, scalable data models.
  • Database Relationship Diagrams: Ability to use tools that visually map out the relationships between different tables and databases to ensure a clear structure for data analysis with tools like dbdiagram.io, diagrams.net, draw.io.
  • Create-A-Derived-Table: Expertise in building derived tables to transform raw data into meaningful, ready-to-use datasets for analysis.
  • Database Tool Learning (dbt): Hands-on experience with dbt (data build tool) for building modular and reusable data transformations.

Building the Data Model Environment and Deployment

  • R-Studio: Familiarity with R-Studio for data analysis and environment management, particularly in the deployment and automation of analytics workflows.
  • Git & GitHub: Version control expertise for managing data models and code, ensuring collaboration, traceability, and proper deployment of changes.
  • Athena and the Analytical Platform: Knowledge of Amazon Athena, a key tool used at MoJ for querying large datasets, often within the MoJ’s Analytical Platform.
  • Macros: Understanding and usage of macros to automate repetitive tasks in data processing.

Data Model Testing

  • AWS Console & Athena Tables: Proficiency in working with AWS services, particularly querying and testing Athena tables for accuracy and performance.
  • SQL Code: Writing and testing SQL code to ensure data transformations and models work as expected.
  • dbt Commands: Using dbt commands to test, build, and validate data models, ensuring high-quality outputs.
  • GitHub Actions: Automating testing and deployment processes using GitHub Actions, ensuring that changes are tested and verified before deployment.
  • Lint Code: Implementing linting tools to ensure clean, maintainable, and error-free code.

Production Ready Assurance

  • Amazon Athena Editor: Experience in using Athena’s SQL editor to test and validate queries before moving them into production.
  • R-Studio Terminal: Using the terminal in R-Studio to execute commands and manage the analytics environment.
  • GitHub Actions: Ensuring that data models and transformations are production-ready by using GitHub Actions for continuous integration and deployment.
  • Linting: Implementing linting processes for code quality assurance before production deployment.
  • Power BI Dashboards: Creating and maintaining production-ready dashboards in Power BI, ensuring they are reliable and up-to-date for end users.

By mastering these technical skills, an Analytics Engineer at the MoJ can ensure efficient data workflows, high-quality data models, and reliable reporting that supports the ministry’s operational and strategic objectives.