Analytics engineering: Six best practices for success

Analytics engineering is where data business understanding, statistical analysis, and technical implementation all meet. The practice fills the gap between data engineers who collect raw data and data analysts who interpret it.

Here are some analytics engineering best practices your organization can follow to achieve the highest possible value from your business’s most precious asset—your data.

What is analytics engineering?

The practice of analytics engineering applies software engineering principles to transform raw data into clean, organized, and analysis-ready datasets. Analytics engineers handle transforming data—cleaning, modeling, testing, and documenting the many different forms of data your company manages. This ensures it can be accessed, analyzed, and acted upon by data analysts and business users across the organization.

Beyond dealing with the data itself, analytics engineering also manages data as it moves through the data lifecycle. Analytics engineers ensure data is consistently and reliably ingested, transformed, scheduled, and properly formatted for analytics use. In some data system architectures, analytics engineers also decide which tools to use for ETL/ELT and then implement and operate them.

The benefits of analytics engineering

Embracing analytics engineering enables efficient data-driven decision-making by ensuring data quality and reliability. Practicing analytics engineering in your data stack offers powerful benefits:

• Creates a single source of truth for business metrics
• Reduces duplicate work across analytics teams
• Ensures data consistency and reliability
• Makes data more accessible to business users
• Speeds up the time from raw data to insights

Analytics engineering best practices

Analytics engineering is more than data transformation and organization. It's about creating a robust, scalable foundation for analytics that enables self-service data access while maintaining data quality and consistency. Here are some well-established best practices for building this foundation within your own organization.

1. One-stop data shop

Providing a single source of truth for your organization’s data is the core value offered by analytics engineering—the primary benefit from which many other benefits flow. This is why auditing and enforcing that everyone is consistently using this single source is absolutely number one on the list of best practices.

Sometimes, though, a team may want to not only own (and also possibly wall off) their own data pipeline. This kind of “shadow IT” scenario typically happens when a group experiences trouble getting the data that they need for their work or the data they are getting is problematic—outdated, faulty, or incorrect.

Best practice: Ensure that everyone who touches data in your org is onboarded and trained to use your data tools for their own most important work. Formal training gets people on board by showing them the deep connection between consistent, global data governance and business value.

2. Data quality management

Clean data is essential. "Garbage in, garbage out" applies even more forcefully when you're responsible for the analytics that others will use to make decisions. Data governance best practices that are key to data quality management include:

• Implementing data quality checks
• Establishing naming conventions
• Ensuring data security and privacy standards

Best practice: Automate everything you can automate. Analytics engineers should implement validation checks, establish data quality metrics, and create automated testing procedures to manage and oversee data quality in your ETL/ELT pipelines. This ensures that there will be no data corruption or loss as data flows between source, transformation, and load or ingestion.

3. Collaboration

Your datasets, no matter how elegantly structured and pristine in quality, need to be user-friendly while answering questions people ask, or they’ll simply be of no value. Analytics engineers need to understand what the business needs to create data models that are both performant and intuitive to business users.

Best practice: Foster collaboration and communication between analytics engineers and their main customers, business teams, and data analysts to better solve their problems using data. Your company’s analytics engineers need to speak data tech and business terms like ROI, retention, KPI, or in marketing, customer acquisition costs (CAC).

4. Documentation and knowledge sharing

Analytics engineers, especially those coming from an initial background in data engineering, need to understand that documentation is more than just code comments. Data dictionaries, transformation logic, and business rules are all critical knowledge centers to be created and shared.

Best practice: Maintain comprehensive documentation of data lineage, modeling decisions, and analytical methodologies. A good data governance tool will assist engineers in creating, tracking, and even automating the writing of solid data documentation.

5. Version control and change management

Analytics engineers coming from the data analytics side of the equation will need to adapt in the opposite way: treating analytics code like software code. Analysts without a background in software development or infrastructure may not have an understanding of the software development lifecycle and operation processes that contribute to analytics engineering.

Best practice: Use git for version control, create development branches, and enshrine code review processes into your data handling lifecycle to maintain stability and collaboration as data moves from source to ingestion.

6. Modular and reusable design

Building analytics as modular components is a truly game-changing best practice. Modular architecture makes your analytics codebase more maintainable and scalable because it reduces code duplication and centralizes documentation, increases test coverage, and ensures consistent business logic across your organization’s data models. When business requirements change, modular component architecture lets analytics engineers modify a single parent module, rather than updating multiple models.

Best practice: Create reusable transformation logic, maintain consistent naming conventions, and design flexible data models that can adapt to changing requirements.

How dbt powers analytics engineering best practices

dbt is central to modern analytics engineering because it brings software engineering best practices to data transformation. At its core, dbt lets you write data transformations in SQL but manage them like software code—making it the perfect bridge between raw data and data-driven business outcomes.

dbt Cloud is the analytics engineering platform that any company can use to accelerate data delivery, increase data quality, and increase data democratization while simultaneously improving compliance and security.

Learn more about how dbt Cloud can be the powerful analytics engineering engine for your organization—ask us for a demo today.