Practice 03
Data Engineering & Analytics
We design and build AI-ready and agent-ready data platforms — lakehouses, streaming mesh architectures and governed catalogues that feed RAG, embeddings pipelines and autonomous analytics workloads.
Follow the architecture
Make every transformation explainable.
Explore a streaming-to-analytics path with ownership, quality and lineage at each stage. Faster data is only valuable when its meaning can be trusted.
Sources — Define the contract
Identify systems of record and the meaning of each field. A data contract makes schema, ownership and expected freshness explicit.
- Name the source owner
- Classify personal and sensitive fields
- Define schema evolution rules
Ingestion — Capture reliably
Decouple producers from consumers and plan for late, duplicate and out-of-order events. Recovery behaviour matters as much as normal throughput.
- Choose replay and retention windows
- Design idempotency and deduplication
- Quarantine malformed events
Quality — Check the meaning
Validate completeness, timeliness and domain rules before downstream consumption. Failed records need an owned remediation path, not silent deletion.
- Measure freshness and completeness
- Check business invariants
- Assign failed-quality alerts
Lakehouse — Govern the record
Organise durable data with catalogues, table formats and access policies. Schema changes and retention rules should be discoverable and testable.
- Track table and column lineage
- Control access by role and purpose
- Plan compaction and lifecycle policies
Consumption — Deliver a decision
Expose curated datasets through appropriate analytics, APIs or AI retrieval. Shared definitions prevent different teams from answering the same question differently.
- Publish metric definitions
- Apply consumer-specific permissions
- Monitor query cost and usefulness
Hover, focus or tap a flow node to reveal the decisions behind it.
Architecture, explored
Security is a system. Not a checkpoint.
Design questions to resolve
Start with a boundary. Build the bigger picture.
These are illustrative patterns, not a description of a deployed client system or a guarantee of compliance. The right architecture depends on your constraints, risks and operating model.
From concept to implementation
Technical scope & design decisions
Explore the questions, techniques and potential outputs that can shape an engagement. Final scope depends on the requirements and evidence available.
01Lakehouse Architecture
Modern lakehouse designs that unify data warehousing and data lake capabilities — enabling batch and streaming analytics, ML feature serving, and governed data sharing at scale.
Design & implementation focus
- Lakehouse architecture design on Databricks, Snowflake and BigQuery
- Table format selection and migration: Delta Lake, Iceberg and Hudi
- Bronze-Silver-Gold medallion architecture design and governance
- Z-ordering, partitioning and liquid clustering for query performance
- Time travel, data versioning and audit trail implementation
- Unity Catalog data governance and fine-grained access control
- Lakehouse federation and data sharing with Delta Sharing
- Cost governance and compute autoscaling for lakehouse workloads
Potential engagement outputs
- Architecture Blueprint
- Table Format Design
- Medallion Model
- Governance Framework
- Performance Baseline
- Cost Model
02Real-Time Streaming Pipelines
High-throughput, low-latency streaming platforms that process millions of events per second — powering real-time analytics, fraud detection, personalisation, and operational intelligence.
Design & implementation focus
- Kafka cluster design with topic partitioning, replication and retention
- Exactly-once semantics with Kafka Transactions and Flink checkpointing
- Apache Flink and Spark Structured Streaming pipeline development
- Schema registry with Avro, Protobuf and JSON Schema evolution
- Change Data Capture with Debezium for real-time database replication
- Streaming lakehouse ingestion with Delta Live Tables and Iceberg Streaming
- Low-latency serving layers with Redis, DynamoDB and Cassandra
- Backpressure handling, dead letter queues and stream monitoring
Potential engagement outputs
- Streaming Architecture
- Kafka Design
- Flink Pipelines
- Schema Registry
- CDC Configuration
- Monitoring Stack
03Data Mesh Architecture
Organisational and technical frameworks for data mesh — enabling domain-oriented data ownership, federated governance, and self-serve data product infrastructure.
Design & implementation focus
- Data mesh operating model design and domain boundary definition
- Data product specification standards and ownership models
- Self-serve data platform tooling for domain teams
- Federated computational governance with centralised policy enforcement
- Data product discoverability with metadata-rich cataloguing
- Interoperability standards: common schemas, APIs and SLAs
- Data product quality scoring and observability integration
- Data mesh maturity assessment and incremental migration roadmap
Potential engagement outputs
- Operating Model
- Domain Design
- Product Standards
- Governance Framework
- Platform Tooling
- Migration Roadmap
04ETL/ELT Pipeline Engineering
Scalable, maintainable data pipeline engineering using modern ELT patterns, dbt transformation frameworks, and cloud-native orchestration for enterprise data estates.
Design & implementation focus
- dbt project design with modular models, tests and documentation standards
- Airflow, Dagster and Prefect orchestration and dependency management
- Incremental load strategies: CDC, watermark and partition-based patterns
- ELT vs ETL evaluation and hybrid architecture design
- Fivetran and Airbyte connector configuration and custom development
- Data quality testing frameworks embedded in pipeline execution
- Pipeline observability with OpenLineage and Marquez lineage tracking
- Idempotency, retry semantics and error recovery design patterns
Potential engagement outputs
- dbt Project
- Pipeline Library
- Orchestration Config
- Data Quality Tests
- Lineage Integration
- Ops Runbook
05Data Governance & Cataloguing
Enterprise data governance frameworks that instil confidence in data quality, lineage, ownership and compliance — enabling data to be trusted and efficiently discovered.
Design & implementation focus
- Data governance operating model: stewardship, ownership and accountability
- Business glossary and data dictionary design and population
- Data catalogue implementation: Collibra, Alation, Dataplex or OpenMetadata
- Automated metadata harvesting and lineage capture from pipelines
- Data quality rule frameworks with Great Expectations or Soda Core
- GDPR data mapping: personal data inventory, retention and deletion
- Master data management for customer, product and reference data
- Data governance maturity assessment and roadmap delivery
Potential engagement outputs
- Governance Framework
- Catalogue Implementation
- Data Dictionary
- Quality Rules
- GDPR Data Map
- MDM Design
06Enterprise Data Warehousing
Modern cloud data warehouse design and engineering on Snowflake, BigQuery, Redshift and Synapse — optimised for analytical performance, governance and cost at scale.
Design & implementation focus
- Data warehouse architecture: Kimball dimensional, Data Vault 2.0 and hybrid
- Snowflake account design: virtual warehouses, resource monitors and workload management
- BigQuery cost optimisation: slot reservation, materialised views and partition pruning
- Redshift cluster design with AQUA, distribution keys and sort keys
- Azure Synapse Analytics workspace and dedicated pool architecture
- Multi-tenant data warehouse design with row-level security
- Performance tuning: clustering, caching, query profiling and workload management
- Data warehouse migration from legacy on-premises platforms
Potential engagement outputs
- Warehouse Architecture
- Schema Design
- Performance Baseline
- Security Model
- Migration Plan
- Cost Optimisation Report
07Analytics & BI Platforms
Semantic layer design and BI platform implementation that delivers trusted, governed analytics to business users — from executive dashboards to self-serve exploration.
Design & implementation focus
- BI platform selection, evaluation and proof of concept delivery
- Semantic layer design with dbt Metrics, Looker LookML and AtScale
- Power BI Premium deployment with row-level security and governance
- Tableau Server and Cloud enterprise configuration and content governance
- Embedded analytics design for product teams and external stakeholders
- KPI framework design and metrics cataloguing aligned to business strategy
- Report migration from legacy platforms to modern cloud BI
- BI adoption programmes and data literacy training for business users
Potential engagement outputs
- BI Architecture
- Semantic Layer
- Dashboard Portfolio
- Governance Guide
- KPI Catalogue
- Training Programme
08Data Platform Engineering
Infrastructure and platform engineering for enterprise data teams — building scalable, self-serve data environments that reduce time-to-insight and engineering toil.
Design & implementation focus
- Databricks workspace architecture with Unity Catalog and cluster policies
- Apache Spark performance engineering: partitioning, caching and stage optimisation
- Distributed compute with Ray and Dask for large-scale Python workflows
- GPU cluster provisioning for ML training workloads on cloud platforms
- Data platform infrastructure-as-code with Terraform and Databricks provider
- Notebook and development environment standardisation with containers
- Data SLAs, observability and incident management for platform reliability
- Platform cost governance with Databricks monitoring and chargeback models
Potential engagement outputs
- Platform Architecture
- Cluster Design
- IaC Templates
- Observability Stack
- Cost Model
- Developer Guide