Contracts over conventions
Producers publish schemas with SLAs. Breaking changes fail the build, not the Monday morning board pack.
Data Foundation
One trusted number, not eleven competing spreadsheets
Analytics fails for structural reasons: unowned pipelines, undocumented transforms and metrics defined differently in every department. We rebuild the foundation — ingestion, modelling, quality, lineage and semantics — so the dashboard everyone trusts is the same one the model trains on.
0+
Pipelines in production
0%
Reduction in report build time
0 PB
Data under management
Overview
A lakehouse that serves BI, ML and operational systems from the same contract
We implement medallion architecture properly: raw landing that is immutable and replayable, a conformed layer with enforced schemas and data contracts, and consumption models shaped for the question being asked. Transformations live in version control, are tested, and are documented automatically.
Governance is not a separate project bolted on afterwards. Catalogue, lineage, classification, row and column-level security, and retention policy are provisioned as infrastructure-as-code alongside the pipelines that need them.
The result is a platform where a new analytics use case takes days instead of a quarter, and where an AI initiative already has the trustworthy historical data it depends on.
Tested transformations
Every model has assertions for uniqueness, referential integrity, freshness and business rules, executed on every run.
One semantic layer
Metrics defined once and consumed identically by Power BI, Tableau, notebooks and APIs.
Cost transparency
Warehouse spend attributed per team and per query pattern, with partitioning and clustering tuned to the actual workload.
What we build
Capabilities you get on day one
The components below are engineered patterns we have shipped repeatedly — not concepts we would be exploring for the first time on your project.
Lakehouse foundation
Open table formats, ACID guarantees, time travel and schema evolution — one copy of data serving SQL, Spark and ML.
Declarative pipelines
Orchestrated DAGs with retries, backfills, idempotency and lineage captured automatically from the code.
Quality gates
Freshness, volume, distribution and business-rule tests that quarantine bad batches before they reach consumers.
Governed BI
Certified datasets, workspace topology, deployment pipelines and row-level security that mirrors your org chart.
Streaming where it matters
Sub-second event pipelines for fraud, telemetry, inventory and personalisation — batch everywhere else, deliberately.
Analytics engineering
dbt projects with modular models, documented interfaces, CI on pull requests and environment promotion.
Capabilities
Everything inside our data engineering & analytics practice
The full scope of the practice. Engagements typically draw on a focused subset — this is the bench you have access to.
Platform & Architecture
- Data warehouse design & modernisation
- Data lake & lakehouse implementation
- Medallion / multi-hop architecture
- Dimensional & Data Vault modelling
- Data mesh & domain ownership models
- Master data management (MDM)
- Streaming architecture design
- Cloud data platform migration
Pipelines & Integration
- ETL & ELT pipeline engineering
- Change data capture (CDC)
- Real-time & streaming ingestion
- Batch orchestration & scheduling
- API & SaaS connector development
- Legacy & mainframe data migration
- Reverse ETL & operational activation
- Data quality & reconciliation frameworks
Big Data & Processing
- Apache Spark engineering
- Databricks platform delivery
- Snowflake implementation & tuning
- Microsoft Fabric & OneLake
- Apache Kafka & event streaming
- Apache Airflow orchestration
- dbt transformation frameworks
- Delta Lake & Apache Iceberg
Analytics & Visualisation
- Power BI development & governance
- Tableau dashboards & server administration
- Real-time analytics & operational dashboards
- Self-service analytics enablement
- Embedded analytics for products
- Financial & regulatory reporting
- Semantic & metrics layer design
- Data storytelling & executive reporting
Governance & Compliance
- Data catalogue & business glossary
- Column-level lineage & impact analysis
- PII discovery & classification
- Row / column-level security
- Data retention & archival policy
- GDPR, HIPAA & CCPA data controls
- Audit logging & access reviews
- Data stewardship operating model
Business impact
The outcomes clients measure
Figures are medians across delivered engagements in this practice. We will baseline your own numbers during discovery rather than promise these.
85%
Faster reporting cycles
Month-end consolidation that took nine days completed overnight, with variance explanations attached.
↓ 55%
Lower warehouse spend
Right-sized compute, workload isolation, incremental models and pruning-friendly table design.
1 source
Of truth for KPIs
A governed semantic layer ends the reconciliation meetings between finance, sales and operations.
4 hrs
To onboard a new dataset
Templated ingestion patterns and contract enforcement turn a project into a pull request.
Technology stack
Data Engineering & Analytics technology stack
Selected per engagement against your existing estate, your team's skills and total cost of ownership — never by partnership tier.
Warehouses & Lakehouses
- Snowflake
- Databricks
- Microsoft Fabric
- BigQuery
- Amazon Redshift
- Azure Synapse
Processing
- Apache Spark
- dbt
- Apache Flink
- Pandas
- Polars
- Trino
Streaming & Ingestion
- Apache Kafka
- Event Hubs
- Kinesis
- Debezium
- Fivetran
- Azure Data Factory
Orchestration
- Apache Airflow
- Dagster
- Azure Data Factory
- Prefect
- Databricks Workflows
BI & Visualisation
- Power BI
- Tableau
- Looker
- Apache Superset
- Grafana
Governance
- Unity Catalog
- Microsoft Purview
- Collibra
- Great Expectations
- OpenLineage
How we deliver
How a data engineering & analytics engagement runs
Six stages, each with a defined output. You can stop after any one of them and still hold something useful.
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Data landscape audit
Inventory sources, consumers, critical reports and the shadow spreadsheets that hold the business together.
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Target architecture
Platform selection, layer design, contract standards, security model and a migration sequence ordered by business risk.
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Foundation build
Provision the platform as code, stand up CI/CD, catalogue, monitoring and the first ingestion patterns.
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Domain onboarding
Migrate source by source with parallel-run reconciliation, so numbers are proven before the legacy report is retired.
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Enablement
Certified datasets, self-service training, stewardship roles and documentation your analysts actually use.
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Optimise & run
Cost tuning, SLA monitoring, quality scorecards and a roadmap for the next domains.
Industries we serve with this practice
Engagement models
How to start with Data Engineering & Analytics
Three commercial shapes. Most clients begin with an assessment and move into delivery once the plan is agreed.
Fixed-price assessment
From $12,000
Two to four weeks. Produces a prioritised backlog, target architecture, risk register and a costed delivery plan you own outright.
- Named architect
- Executive readout
- No obligation to proceed
Most common
Milestone delivery
Scoped per phase
Well-bounded phases priced against agreed acceptance criteria. Suited to migrations, integrations and defined product increments.
- Acceptance-based billing
- Change control included
- Fortnightly demos
Dedicated pod
Monthly retainer
An embedded team — lead, engineers, QA — working in your sprints and tooling with US-hours overlap from our India centre.
- Scale up or down monthly
- Your definition of done
- Direct team access
Indicative ranges for planning purposes. Final pricing follows scope confirmation — we do not quote before we understand the problem.
FAQs
Data Engineering & Analytics — frequently asked
Snowflake, Databricks or Microsoft Fabric — which should we choose?
It depends on your existing estate and workload mix. Databricks leads where Spark and ML dominate; Snowflake is exceptional for SQL analytics with elastic isolation; Fabric is compelling when you are already deep in Microsoft 365 and Power BI. We run a short structured evaluation against your real workloads and a five-year TCO model rather than defaulting to one vendor.
Do we have to replace our existing warehouse?
Usually not immediately. We commonly place a new governed layer alongside the incumbent, migrate domain by domain with parallel-run reconciliation, and decommission legacy reports only once the numbers match. That keeps the business reporting uninterrupted throughout.
How do you guarantee the numbers are correct after migration?
Reconciliation is a deliverable. For every migrated report we run source and target side by side across historical periods, produce a variance report, and require sign-off from the report owner before cutover. Differences are explained — often the new number is right and the old one was silently broken.
Can you work with on-premise data that cannot move to the cloud?
Yes. We deliver hybrid patterns using self-hosted integration runtimes, on-premise Spark, or federated query engines so sensitive data stays in place while metadata and aggregates flow to the cloud platform.
Who owns the platform after you leave?
Your team. Everything is infrastructure-as-code in your repositories, documented, with runbooks and a handover programme. We offer managed operations if you want it, but never as a dependency created by opacity.
Related practices
AI & Machine Learning
Production-grade generative AI, applied ML and autonomous agents — governed, evaluated and observable.
Cloud Services
Migration, cloud-native engineering, Kubernetes, FinOps and resilience across AWS, Azure, Google Cloud, Oracle and IBM.
Microsoft Services
Azure, Microsoft 365, Power Platform, Dynamics 365, Fabric and Copilot — delivered by a Microsoft-first practice.
DevOps & Platform Engineering
CI/CD, GitOps, Kubernetes, observability and internal developer platforms that make releases unremarkable.
Data Foundation
Ready to talk about data engineering & analytics?
Send the context — current systems, constraints, what you have already tried. An architect from this practice will reply, usually within one business day.