Analytics Engineering Explained: Bridging Data Engineering and Analysis

Analytics engineering is a discipline that applies software engineering practices to the transformation of raw data into clean, reliable datasets ready for analysis. Analytics engineers sit between data engineers who build data infrastructure and data analysts who consume data for insights, ensuring that data is trustworthy, well-documented, and structured for use.

The role emerged from recognition that traditional organizational structures created gaps where nobody owned data transformation quality and maintainability.

The Problem Analytics Engineering Solves

The Analyst Bottleneck

Traditional analysts spend enormous time on data preparation:

• Joining tables across systems
• Cleaning inconsistent data
• Recreating the same transformations repeatedly
• Debugging data quality issues

This leaves little time for actual analysis.

The Engineer Gap

Data engineers excel at infrastructure but may lack business context:

• Pipelines move data without adding meaning
• Raw data lands in warehouses without transformation
• Business logic scattered across report queries
• No consistency between analyses

Technical excellence doesn't ensure analytical usefulness.

The Documentation Void

Tribal knowledge dominates:

• "Ask Sarah how that metric is calculated"
• Logic buried in report queries
• No single source of truth
• New team members struggle for months

Undocumented data creates organizational risk.

The Quality Lottery

Data quality varies unpredictably:

• Some reports use tested data, others don't
• Quality issues discovered during board meetings
• No systematic validation
• Every analyst builds their own cleaning logic

Inconsistent quality undermines trust.

What Analytics Engineers Do

Data Modeling

Transform raw data into analytical models:

Staging models: Clean and standardize raw source data.

Intermediate models: Business logic and joins.

Mart models: Final datasets optimized for specific use cases.

Good models make analysis easy and reliable.

Testing

Validate data quality systematically:

Schema tests: Data types, not-null constraints, uniqueness.

Business rules: Revenue matches orders, dates are valid.

Referential integrity: Foreign keys exist in parent tables.

Custom validations: Business-specific quality checks.

Tests catch issues before users do.

Documentation

Make data understandable:

Column descriptions: What each field means in business terms.

Model descriptions: What each table represents and contains.

Lineage documentation: Where data comes from and how it transforms.

Usage examples: How to use data correctly.

Documentation enables self-service.

Version Control

Apply software engineering rigor:

Git workflows: Branch, review, merge.

Change history: Track what changed and why.

Collaboration: Multiple people can work without conflict.

Rollback capability: Undo problematic changes.

Version control provides accountability and safety.

Automation

Continuous integration and deployment:

Automated testing: Tests run on every change.

Automated deployment: Changes deploy without manual steps.

Scheduled runs: Models update on defined schedules.

Alerting: Notifications when things fail.

Automation ensures reliability and frees time for higher-value work.

Analytics Engineering Practices

Modularity

Build small, focused models:

• Each model does one thing well
• Models compose to create complexity
• Changes affect limited scope
• Reuse reduces duplication

Small pieces are easier to understand and maintain.

Declarative Transformations

Define what you want, not how to get it:

-- Declarative: What is a customer's total orders?
SELECT
    customer_id,
    SUM(order_total) as lifetime_value
FROM orders
GROUP BY customer_id

Let the database optimize execution.

Idempotency

Models can run repeatedly with same results:

• Full refresh creates identical output
• Incremental models handle reprocessing
• No accumulated side effects
• Safe to retry failures

Idempotency enables reliable automation.

Testing First

Test before trusting:

• Define expectations before building
• Test data, not just code
• Catch issues in development
• Prevent regressions

Testing is investment, not overhead.

Documentation as Code

Documentation lives with code:

• Update documentation with changes
• Generate documentation automatically
• Keep documentation in sync
• Make documentation accessible

Codd Semantic Layer Automation extends analytics engineering practices by automatically generating semantic definitions from transformation code, ensuring that business context stays synchronized with technical implementation.

The Analytics Engineering Workflow

Understand Requirements

Start with the business need:

• What decision needs to be made?
• What questions need answering?
• Who will use this data?
• How frequently is data needed?

Requirements drive design.

Design the Model

Plan before building:

• What sources are needed?
• What transformations apply?
• How should data be structured?
• What tests ensure quality?

Design prevents rework.

Develop Iteratively

Build in small increments:

• Create staging model, test, commit
• Add intermediate logic, test, commit
• Build final mart, test, commit

Small steps are easier to verify.

Review Changes

Get feedback before deploying:

• Code review from peers
• Business review from stakeholders
• Test results validate correctness

Review catches issues early.

Deploy and Monitor

Release with confidence:

• Automated deployment
• Monitor for failures
• Track data quality metrics
• Alert on issues

Production requires vigilance.

Analytics Engineering Tools

Transformation Tools

dbt (data build tool) dominates but alternatives exist:

• SQL-based transformation with templating
• Testing and documentation built in
• Version control integration
• Growing ecosystem

Choose tools that match your skills and needs.

Data Warehouses

Modern warehouses enable analytics engineering:

• Snowflake, BigQuery, Redshift, Databricks
• Scalable compute for transformations
• SQL as primary interface
• Integration with transformation tools

Warehouse choice affects tooling options.

Orchestration

Schedule and coordinate workflows:

• Airflow, Dagster, Prefect
• Trigger transformations on schedule
• Manage dependencies
• Handle failures gracefully

Orchestration ensures timely data.

Quality Monitoring

Track data health over time:

• Anomaly detection
• Trend monitoring
• Alert management
• Root cause analysis

Monitoring complements testing.

Building an Analytics Engineering Practice

Start with Foundation

Establish basics first:

• Version control for all SQL
• Basic testing framework
• Documentation standards
• Development workflow

Foundation enables growth.

Migrate Existing Work

Transform legacy analytics:

• Identify critical reports and queries
• Extract transformation logic
• Rebuild as documented, tested models
• Deprecate legacy sources

Migration takes time but pays off.

Establish Standards

Define how work gets done:

• Naming conventions
• Modeling patterns
• Testing requirements
• Review processes

Standards ensure consistency.

Scale the Practice

Grow as value proves out:

• Train analysts on engineering practices
• Hire dedicated analytics engineers
• Expand model coverage
• Increase automation

Success breeds investment.

Analytics Engineering Challenges

Skill Gaps

The role requires hybrid skills:

• SQL fluency
• Software engineering practices
• Business domain knowledge
• Communication abilities

Finding or developing this combination takes effort.

Organizational Fit

Where do analytics engineers belong?

• Data engineering team?
• Analytics team?
• Separate team?

Organizational placement affects effectiveness.

Scope Creep

Analytics engineers can become bottlenecks:

• Every request routes through them
• Queue grows faster than capacity
• Original flexibility lost

Balance building foundations with enabling others.

Legacy Dependencies

Existing reports depend on old patterns:

• Can't break production reports
• Migration requires parallel operation
• Technical debt accumulates

Plan migration carefully.

Analytics Engineering and AI

Analytics engineering creates foundations for AI-powered analytics:

Quality data: Tested, documented data feeds reliable AI.

Clear semantics: Well-defined models help AI understand meaning.

Consistent logic: Standard calculations prevent AI confusion.

Traceable lineage: AI can explain where data came from.

Organizations with mature analytics engineering practices are better positioned to deploy AI analytics that users can trust.

Getting Started

Organizations building analytics engineering capabilities should:

1. Assess current state: How is transformation done today?
2. Choose tools: Select transformation and testing tools
3. Establish practices: Define workflows and standards
4. Start small: Migrate one domain or data source
5. Demonstrate value: Measure quality and productivity gains
6. Expand systematically: Grow based on proven success

Analytics engineering transforms data transformation from ad-hoc craft to disciplined practice, enabling trustworthy analytics at scale.