Order, customer, catalog, and fulfillment records.
SQL · files All projects
PROJECT WALKTHROUGH / ANALYTICS
Analytics Data Platform
Create curated dimensional models, reusable transformations, data marts, and business-ready datasets.
ARCHITECTURE / LEARNING VIEW01Operational sources 02Load and stage 03Base models 04Intermediate models 05Marts 06Analytics consumers
Land source-aligned data and record freshness.
SnowflakeCast types, standardize keys, and document sources.
dbt · SQLApply reusable joins and business rules.
dbtPublish conformed dimensions, facts, and metrics.
SnowflakeQuery stable, tested business datasets.
SQL · BI DESIGN WALKTHROUGH6 LAYERS
PROJECT OVERVIEW
Understand the system before building it.
A learning design for a shared analytics platform that organizes source data into tested transformation layers and understandable business-facing models.
WHAT YOU WILL EXPLORETranslate stakeholder questions into model grainBuild reusable and testable transformationsExplain metric definitions and data lineage
01
START WITH THE WHY
Business problem
Make the data need understandable before choosing tools or designing pipelines.
A generic commerce team has several dashboards that compute similar measures differently. Analysts need documented definitions and consistent, reusable datasets.
Data engineering provides a repeatable way to move, validate, transform, and make data available with clear ownership and operational expectations.
The platform design creates curated marts with shared definitions, documented lineage, and checks that help consumers interpret data consistently.
02
DEFINE THE BOUNDARIES
Requirements
Separate what the workflow must do from the reliability and operational qualities it needs.
- Model customer, order, product, and fulfillment data
- Publish reusable metrics and marts
- Support documented refresh and freshness expectations
- Use configuration rather than hard-coded environment-specific values.
- Add validation, structured run logging, and clear failure boundaries.
- Protect credentials and grant only the access each workload needs.
- Measure freshness, duration, volume, and quality outcomes.
03
FOLLOW THE DATA
Architecture
A layered view of how data moves from source systems to a useful consumer-facing output.
Order, customer, catalog, and fulfillment records.
SQL · filesLand source-aligned data and record freshness.
SnowflakeCast types, standardize keys, and document sources.
dbt · SQLApply reusable joins and business rules.
dbtPublish conformed dimensions, facts, and metrics.
SnowflakeQuery stable, tested business datasets.
SQL · BIConceptual learning architectureSpecific services depend on requirements, constraints, and deployment choices.
04
KNOW WHAT ENTERS THE SYSTEM
Source systems
Map each source to its data shape, arrival pattern, ingestion choice, and likely failure modes.
SOURCE / 01Relational tables
Commerce transactions
Example data
Order headers, line items, and status changes
Frequency
Incremental daily
Ingestion
Load into source-aligned staging models
Potential issues
Inconsistent status definitionsLate cancellationsRepeated keys
SOURCE / 02Table and periodic file
Product catalog
Example data
SKU, category, and product attributes
Frequency
Daily snapshot
Ingestion
Load snapshot and compare changes
Potential issues
Category remappingNull descriptionsDuplicate SKU values
05–07
MOVE, SHAPE, STORE
Implementation flow
Build the pipeline in observable stages so each boundary can be tested and recovered independently.
Data ingestion
- Document source freshness and load boundaries.
- Land source data before applying consumer-specific definitions.
- Make model dependencies explicit with source and ref relationships.
- Schedule tests and transformation runs with observable outcomes.
SourcePipelineLandingRaw
Transformation
- Create staging models that standardize source fields.
- Build intermediate models around reusable business logic.
- Declare fact grain and dimension relationships.
- Use shared metric definitions and document assumptions.
-- Illustrative quality check
SELECT business_key, COUNT(*) AS row_count
FROM staged_records
GROUP BY business_key
HAVING COUNT(*) > 1;Storage / warehouse
- Keep source-aligned staging separate from consumer marts.
- Use fact tables for events and dimensions for descriptive context.
- Expose stable views or marts with documented grain.
- Use incremental models only when change semantics are understood.
08
TRUST THE OUTPUT
Data quality
Make quality expectations explicit and decide what happens when a record or batch does not pass.
Incoming records
ValidationSchema · rules · keys
Valid recordsCurated target
!Invalid recordsReason · run ID · quarantine
- Test primary business keys and expected grain.
- Check relationships between facts and dimensions.
- Validate accepted values and non-null critical fields.
- Compare freshness and aggregate reconciliation to source expectations.
09–12
RUN IT RESPONSIBLY
Operations, performance, security & monitoring
A realistic project also explains how the system behaves when inputs change, work slows down, or something fails.
09 / Error handling
- Surface model and test failures with upstream context.
- Keep failed transformations from being treated as approved releases.
- Provide targeted full-refresh or bounded reprocessing guidance.
- Record run and model-version metadata for audits.
10 / Performance
- Select only needed columns and filter within model boundaries.
- Review join cardinality and avoid accidental fanout.
- Use incremental materialization when measured and correct.
- Check warehouse query profile and model run duration.
11 / Security
- Separate developer, transformation, and consumer roles.
- Restrict raw customer attributes and publish only needed columns.
- Apply masking or row-level controls if the domain requires them.
- Keep credentials in managed configuration, never in model SQL.
12
Monitoring and observability
Monitor system health and data health together: pipeline status alone does not tell you whether the delivered data is fresh and complete.
- Track model run status, duration, freshness, and test failures.
- Expose lineage and ownership for critical metrics.
- Alert on failed production tests or late source data.
- The monitoring flow is a project design, not an active ARTECH platform.
OBSERVABILITY DESIGNCONCEPT
Pipeline statusRun state
Job durationElapsed time
Record countsRead · written · rejected
FreshnessLast successful data time
Data qualityRule outcomes
Access auditWho · what · when
Analytics Data Platform monitoring signals shown as a design concept. No live pipeline or alert integration is connected.
13
PROMOTE WITH CONTROL
Deployment flow
Treat infrastructure, SQL, configuration, and validation as reviewed changes that move through separate environments.
01DevelopmentBuild and iterate
02TestingRun checks
03StagingValidate release
04ProductionOperate and observe
Git branches and pull requestsCI checks and data testsEnvironment-specific configurationDeployment validation and rollback plan
Deployment workflow is a learning design. No CI/CD pipeline is implemented by this walkthrough.
14
THINK THROUGH TRADE-OFFS
Challenges and solutions
Strong project explanations show the problem-solving process, not only the happy path.
Two teams define net sales differently.
Possible approach
Document the business definition, owner, exclusions, and effective date before creating a shared metric.
A join unexpectedly increases fact row counts.
Possible approach
Check relationship cardinality and model grain, then test uniqueness on the intended dimension key.
An upstream source renames a field.
Possible approach
Detect schema changes in staging and update downstream models through reviewed, tested changes.
15
MAKE THE WORK CLEAR
How to explain this project in an interview
Use this outline to structure a truthful explanation. Adapt it to work you personally completed; this sample is not a claim about your experience.
EXPLANATION STRUCTURE
01Business problem
02Your role and scope
03Architecture and data flow
04Technology choices
05Challenges and solutions
06Quality, security, and performance
07Deployment and outcome
EXAMPLE INTERVIEW EXPLANATIONExample interview explanation: “This analytics platform stages commerce sources, then uses modular SQL transformations to produce conformed dimensions, facts, and consumer marts. I would define grain and metric meaning before implementing joins, then test uniqueness, relationships, required fields, and freshness. Versioned models and pull-request checks support controlled changes, while lineage helps consumers understand where a metric comes from. Performance work would start with model run times, query plans, and observed join cardinality. This is a generic learning example, not a claim about a deployed company platform.”
PROJECT-SPECIFIC QUESTIONS
Practice the follow-up.
01
How do you decide fact table grain?
02
How do tests and documentation help analytics consumers?
03
How would you prevent join fanout?
04
When is an incremental model appropriate?
05
How do you align metric definitions across teams?
PROJECT CHECKLIST
Work through the build.
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