Files or tables arrive with declared schema expectations.
SQL · CSV All projects
PROJECT WALKTHROUGH / DATA QUALITY
Data Quality Framework
Build reusable validation for schemas, nulls, duplicates, referential integrity, freshness, and reconciliation.
ARCHITECTURE / LEARNING VIEW01Input datasets 02Schema validation 03Rule evaluation 04Valid records 05Quarantine 06Quality summary
Check required columns and compatible data types.
Python · SQLApply null, duplicate, reference, and freshness rules.
Quality checksPublish rows that pass configured quality gates.
Warehouse tablesStore rejected rows with rule and run context.
Reject tableExpose counts, outcomes, and trends for review.
Audit view DESIGN WALKTHROUGH6 LAYERS
PROJECT OVERVIEW
Understand the system before building it.
A reusable quality framework design that treats checks as observable pipeline outputs. It demonstrates how valid records can continue while invalid records are routed for investigation with clear reasons.
WHAT YOU WILL EXPLORETurn data expectations into explicit checksSeparate blocking failures from row-level rejectsReconcile source and target outputs
01
START WITH THE WHY
Business problem
Make the data need understandable before choosing tools or designing pipelines.
A generic care analytics team receives records from multiple systems. Missing identifiers, inconsistent types, and duplicate submissions can undermine downstream reporting if they are not detected early.
Data engineering provides a repeatable way to move, validate, transform, and make data available with clear ownership and operational expectations.
The framework makes data-quality outcomes visible and provides a consistent route for valid records, invalid records, and run-level summaries. It uses no real patient data.
02
DEFINE THE BOUNDARIES
Requirements
Separate what the workflow must do from the reliability and operational qualities it needs.
- Apply reusable checks to configured datasets
- Route invalid records with reason codes
- Provide run-level quality summaries
- 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.
Files or tables arrive with declared schema expectations.
SQL · CSVCheck required columns and compatible data types.
Python · SQLApply null, duplicate, reference, and freshness rules.
Quality checksPublish rows that pass configured quality gates.
Warehouse tablesStore rejected rows with rule and run context.
Reject tableExpose counts, outcomes, and trends for review.
Audit viewConceptual 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 / 01CSV and relational extracts
Generic clinical operations export
Example data
Pseudonymous visit keys, event dates, and status codes
Frequency
Daily batch
Ingestion
Schema-checked file/table load using documented contracts
Potential issues
Missing required keysUnexpected codesDuplicate submissionsLate corrections
05–07
MOVE, SHAPE, STORE
Implementation flow
Build the pipeline in observable stages so each boundary can be tested and recovered independently.
Data ingestion
- Register each dataset's schema and quality contract.
- Validate columns and basic types before business transformations.
- Attach dataset, batch, and source metadata to each run.
- Separate check results from records so outcomes remain inspectable.
SourcePipelineLandingRaw
Transformation
- Normalize dates, codes, and casing before rule evaluation.
- Check not-null and accepted-value constraints.
- Detect duplicates using documented business keys.
- Validate reference relationships and source-to-target totals.
# Illustrative transformation outline
valid = [row for row in records if is_valid(row)]
rejected = [row for row in records if not is_valid(row)]
write_curated(valid)
write_quarantine(rejected, run_id=run_id)Storage / warehouse
- Keep raw inputs immutable for bounded audit and replay.
- Publish passing rows to curated targets.
- Store rejected rows with rule ID, reason, and run ID.
- Keep rule configuration versioned alongside transformation code.
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
- Schema and data-type validation.
- Null, uniqueness, duplicate, and accepted-value checks.
- Referential integrity and freshness checks.
- Row-count, aggregate, and source-to-target reconciliation.
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
- Distinguish row-level rejects from dataset-level blocking errors.
- Make failure policy configurable by rule severity.
- Retain invalid records for authorized investigation.
- Provide clear counts and identifiers for a targeted rerun.
10 / Performance
- Push simple validation into the processing engine where appropriate.
- Avoid repeated full scans for checks over the same batch.
- Use bounded incremental comparisons for large histories.
- Measure rule cost and avoid unnecessary wide transformations.
11 / Security
- Use synthetic or pseudonymous examples in learning environments.
- Restrict raw and quarantined data more strongly than curated aggregates.
- Mask sensitive fields in logs and error messages.
- Apply least-privilege access and retention rules.
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 pass rate, reject count, rule failures, and freshness.
- Trend quality outcomes by dataset and rule version.
- Alert on critical rule thresholds and unexpected volume shifts.
- Dashboards shown are monitoring design concepts, not live integrations.
OBSERVABILITY DESIGNCONCEPT
Pipeline statusRun state
Job durationElapsed time
Record countsRead · written · rejected
FreshnessLast successful data time
Data qualityRule outcomes
Access auditWho · what · when
Data Quality Framework 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.
A new source value fails an accepted-value rule.
Possible approach
Quarantine and review the value with a domain owner before safely updating the contract.
A check reports many duplicates but the business key is unclear.
Possible approach
Define uniqueness at the business grain with stakeholders before applying destructive deduplication.
A row-level reject count rises but the pipeline still succeeds.
Possible approach
Set rule severity and thresholds explicitly so acceptable rejects differ from release-blocking failures.
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: “The framework applies versioned expectations at dataset boundaries. It validates schema, required fields, accepted values, uniqueness, referential integrity, freshness, and reconciliation. Passing records proceed to curated targets; row-level failures enter quarantine with rule and run context, while blocking failures stop the dataset according to configured severity. A run summary tracks pass rates and rejects. Sensitive fields are minimized in logs and raw or quarantined data has restricted access. This is a design walkthrough rather than an implemented ARTECH quality service.”
PROJECT-SPECIFIC QUESTIONS
Practice the follow-up.
01
How do you distinguish a warning from a blocking quality failure?
02
How do you define duplicate identity?
03
What belongs in a quarantine record?
04
How do you reconcile data from source to target?
PROJECT CHECKLIST
Work through the build.
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