From regional silos to one observable data factory.
Designing a scalable, API-first AWS data foundation for complex global data enrichment, integration and ETL operations—using cloud-native batch processing and Lean Six Sigma controls.
Conceptual topology · shared standards, policy-aware data placement
- Business problem
- Fragmented workflows and repeated manual work constrained global data operations.
- Adam’s contribution
- Adam led data automation, connecting standardised workflows, reusable engineering and operational measures.
- What this demonstrates
- More repeatable processing and better visibility into failures provided a foundation for scalable operations. No quantified savings are claimed.
Transforming the operating model, not simply relocating ETL.
Global enrichment, integration and ETL activity had evolved into a fragmented collection of local processes, toolchains and specialist knowledge. Capacity was constrained by available labour; diagnosis depended on engineers; routine delivery was vulnerable to individual expertise.
The transformation introduced a standard data supply-chain pattern, reusable Python automation and API contracts, a layered Amazon S3 data lake, and elastic AWS Batch processing on EC2 Spot. Apache Airflow and Lambda supported workflow orchestration and automation; OpenTelemetry made processing behaviour visible. DMAIC provided the operating discipline to measure, improve and control outcomes.
Local optimisation had become a global constraint.
The same enterprise capability was operated differently around the world, with substantial variation in process, tooling and knowledge ownership.
Fragmented execution
Different geographies maintained distinct ETL processes, infrastructure and tools. Exceptions multiplied and reuse was limited.
Labour-bound scale
Output increased largely with human effort, not available compute. Repetitive manual steps absorbed skilled capacity.
Brittle business-as-usual
Key-person dependencies, hand-offs and undocumented workarounds made run-to-run reliability difficult to sustain.
Opaque failure modes
Self-service diagnostics were absent; understanding a failed or stalled run often required engineering-level investigation.
How do we create one governed processing model that works across regions, scales economically with demand, and makes exceptions diagnosable without an expert in the loop?
Standardise the mainline. Isolate legitimate exceptions.
The pivotal process decision was to define a common data supply-chain workflow, with geographic differences expressed through configuration, adapters and explicit policy gates rather than forks in the core process.
- Receive
- Validate
- Enrich
- Integrate
- Quality gate
- Publish
Illustrative canonical workflow; configurable by workload and jurisdiction.
Reusable components replace hand-crafted scripts.
Stable contracts define inputs, outputs and failures.
Controlled exceptions preserve necessary regional differences without proliferating workflows.
An elastic data foundation with a clear separation of concerns.
Orchestration coordinates dependencies. Batch compute executes repeatable transformations. Storage retains governed datasets. APIs expose reusable capabilities. Telemetry connects those layers into an intelligible system.
API-first intake
Stable job and dataset contracts; regional adapters; validated parameters and run identifiers.
APIs · PythonOrchestration
Reusable DAGs, dependency control, scheduled/event triggers and explicit task states.
Apache Airflow · LambdaElastic batch compute
Scale-out, queue-driven transformation and enrichment with interruption-aware processing.
AWS Batch · EC2 SpotLayered S3 data lake
Make the platform reusable, interruptible and governable.
AWS services are necessary infrastructure, not the architecture itself. The durable value comes from well-defined unit-of-work contracts and operational safeguards that make automation safe at enterprise scale.
Contract-led processing
Define versioned request and result schemas, reusable validation/enrichment operations, error taxonomy and idempotency keys. Make each run reproducible and auditable.
API-first · Python · data contractsSpot-aware workload design
Use bounded jobs, safe retries and checkpoints for interruption-tolerant workloads; choose the compute queue and fallback according to business criticality.
AWS Batch · EC2 Spot · resilienceMedallion data stewardship
Preserve recoverable raw inputs; promote datasets only through documented validation and standardisation gates; expose curated outputs with traceable lineage.
Amazon S3 · schema checks · quality gatesDiagnostics as a product
Join process IDs, job IDs and trace IDs so operations teams can identify failed stages, waiting states and common defect causes without code-level triage.
OpenTelemetry · service runbooksGlobal logic, regional policy
Consolidate standards and reusable assets, while making storage placement, transfer approvals, retention and execution locality policy-driven.
Data residency · least privilege · securityChange with measurable control
Instrument the workflow before optimisation; manage exception classes and defects explicitly; validate changes against operational baselines.
DMAIC · process ownership · release gatesControl the transformation with operational evidence.
DMAIC connected architecture and engineering interventions to repeatable operational measures, rather than treating cloud migration as an end in itself.
- D01
Define
Frame fragmented global delivery, workforce dependence, process delays and brittle BAU as measurable failure modes.
- M02
Measure
Capture event timestamps, throughput per sprint, external waiting time and defect counts at consistent boundaries.
- A03
Analyse
Segment by process type and region; decompose active versus blocked time and identify recurring hand-off and defect drivers.
- I04
Improve
Deploy a common workflow, reusable APIs, automated quality gates and elastic batch execution; simplify exception paths.
- C05
Control
Monitor process health through telemetry and scorecards; assign ownership and use runbooks and improvement reviews.
Measure the process, including the time when nothing happens.
Cycle time alone masks the source of delay. OpenTelemetry instrumentation and explicit workflow states make active execution, external waiting, retries and failure modes separately observable.
One process run · multiple accountable states
End-to-end cycle time
Elapsed time from accepted request to completed, quality-approved output. Segment by process class, region and percentile—not just the mean.
Sprint throughput
Count of successfully completed, quality-approved processes in a defined sprint. Use consistent inclusion rules for rework and reruns.
External waiting time
Cumulative time a run is blocked awaiting outside input. Report as absolute duration and share of cycle time; keep queue time distinct.
Process defect count
Unique confirmed defects per run, categorised by cause and severity. Deduplicate recurring alarms and distinguish defects from expected retries.
Recommended supplementary controls: data quality gate-pass rate, first-pass yield, retry/Spot interruption frequency, queue wait, cost per completed job and agreed SLO attainment.
A new unit of scale: the observable processing job.
The architecture was designed to address the structural sources of delay, fragility and labour dependency.
Compute-led scalability
Elastic batch execution reduces the need to scale routine processing in direct proportion to specialist labour.
Reusable data supply chains
Shared workflows, automation modules and S3 layers replace isolated regional implementations with repeatable patterns.
Diagnosable business-as-usual
Instrumented runs and visible failure states make first-line investigation more accessible to data operations teams.
Evidence-led improvement
Common definitions for cycle time, throughput, waiting and defects establish a foundation for prioritisation and ongoing control.
Telemetry should lead to action.
- Monitor
- Diagnose
- Prioritise
- Remediate
- Validate
The strategic asset was not a collection of AWS services. It was a production system for repeatable data work.
An exemplar enterprise data foundation combines process discipline, composable capabilities, governed data, workload-appropriate compute and first-class observability. Cloud elasticity only delivers lasting value when the unit of work is standardised, reliable, independently diagnosable and measurable.
Execution hallmarks
- Translate organisational pain into explicit service and process contracts.
- Decouple orchestration, compute, storage and consumer interfaces.
- Design regional compliance and failure recovery into the foundations.
- Prove improvement through instrumented DMAIC controls, not assumption.
