Data & Analytics
Pipelines, warehousing and BI that turn data into decisions.
Most teams do not have a data problem — they have a dozen of them: numbers living in spreadsheets, an export that never matches the dashboard, a question that takes three days to answer. We have spent years untangling exactly that.
We build the full chain: extract data from wherever it lives, clean and model it properly, run the analysis, and surface it as a live dashboard or a clear written answer. The goal is never a 40-page report nobody opens — it is a decision you can make on Monday, with numbers you can defend.
Whether you need a one-off analysis or a pipeline that refreshes automatically, the work is documented and handed over so it stays yours. Data is one capability in a full-spectrum studio — which means the insights we surface can feed directly into an AI model, a software product or a strategic plan without a disruptive handoff.
- One source of truth instead of conflicting spreadsheets and mismatched exports
- Questions answered in minutes, not days or weeks
- Decisions backed by numbers your team can trust and defend
Capabilities
Data engineering & pipelines
ETL/ELT pipelines, scheduled refreshes and event streams that keep clean, modelled data flowing without anyone touching a spreadsheet.
Data warehousing & modelling
Warehouse setup on BigQuery, Snowflake or Redshift, with dbt models that give every team a consistent, documented source of truth.
Collection & extraction
API integrations, web scraping, PDF and OCR extraction, database pulls and survey design — gathered legally, reproducibly and with clear provenance.
Cleaning & transformation
De-duplication, missing-value handling, outlier treatment, normalisation and turning raw exports into an analysis-ready, well-documented model.
Analysis, EDA & statistics
Exploratory analysis, segmentation, cohort and funnel analysis, hypothesis testing, regression and sampling design — with methods that hold up to scrutiny.
Forecasting & demand planning
Time-series forecasting, demand and capacity planning, and scenario modelling that gives operations teams a reliable look around the corner.
Dashboards & BI reporting
Interactive dashboards in Power BI, Tableau, Looker or a custom web application — live, role-aware and readable at a glance by non-technical stakeholders.
Deliverables
- A clean, documented dataset and data model you own
- Analysis notebook or written report in plain language
- Live dashboards (Power BI, Tableau, Looker or custom web app)
- Repeatable, scheduled pipelines with a walkthrough so your team can operate them
- A metrics and KPI framework with consistent definitions across every stakeholder
Best suited for
- Teams drowning in spreadsheets and conflicting reports from different systems
- Founders who need to validate an idea or a unit-economics thesis with real numbers
- Operations and finance teams who need reliable forecasting and planning data
- Anyone who needs one dashboard the whole organisation trusts
What we build with
Modern data engineering & analytics
Modern data engineering is the practice of building reliable, scalable pipelines that move raw operational data into a form analysts and decision-makers can actually trust. The discipline has shifted decisively from ETL — where transformations happened in a brittle middleware layer before data landed — to ELT, where raw data is loaded into a cloud warehouse or lakehouse first, then transformed in-place using SQL and dbt. That shift matters because the warehouse is now the most powerful compute you have, schema changes are survivable when you have the raw layer, and every transformation is a versioned, testable artifact rather than a black box in a SSIS package.
The lakehouse pattern extends that thinking to semi-structured and high-volume data that once lived only in object storage. Open table formats such as Delta Lake and Apache Iceberg bring ACID transactions, time-travel, and partition evolution to parquet files on S3 or GCS, collapsing the traditional data-lake / data-warehouse split into a single medallion architecture: bronze (raw), silver (cleansed and conformed), gold (aggregated and business-ready). Column-level lineage threads through all three layers so that any analyst can trace a metric back to its origin row.
The final frontier is the semantic layer — a translation layer that sits between the warehouse and every BI tool, embedding metric definitions, slowly-changing dimension logic, and access controls in one place rather than scattered across fifty Tableau workbooks. When the semantic layer is authoritative, 'monthly active users' means exactly the same thing whether the question is asked in Power BI, a Jupyter notebook, or the CEO's mobile dashboard. The path from raw data to trusted decisions runs through ingestion, storage, transformation, quality enforcement, and finally a governed serving layer — and every stage must be observable, idempotent, and version-controlled.
Data engineering lifecycle
Each stage produces testable artifacts and hands off to the next with explicit contracts. Skipping a stage does not accelerate delivery — it defers breakage.
- Catalog source systems and agree freshness SLAs per domain (e.g., orders CDC < 5 min, finance batch nightly)
- Stand up CDC connectors (Debezium on PostgreSQL WAL, Kafka connect for MySQL binlog) or managed EL tools (Airbyte, Fivetran) for SaaS sources
- Design idempotent load patterns: truncate-and-reload for small dimensions, append-only with dedup for events, merge/upsert for CDC streams
- Capture source schema versions and store raw payloads in the bronze layer before any transformation
- Alert on extraction lag, row-count deltas, and connection failures before downstream jobs run
- Partition bronze tables by ingestion date; register them in a table catalog (Unity Catalog, Glue, BigQuery dataset) with column-level descriptions
- Apply medallion architecture: bronze = raw with ingestion metadata, silver = cleansed and type-cast with primary-key dedup, gold = star-schema fact/dimension tables optimized for BI
- Implement slowly-changing dimensions (SCD Type 1 for overwrites, Type 2 for historical tracking) in the silver or gold layer
- Configure Delta Lake or Iceberg for ACID transactions, time-travel queries, and automatic file compaction
- Document grain, surrogate key strategy, and relationship cardinality for every gold-layer table in a data dictionary
- Scaffold the dbt project with source definitions (schema.yml), staging models that cast and rename only, intermediate models for multi-source joins, and mart models for reporting
- Enforce a one-model-one-grain rule: a fact table should never silently aggregate; aggregation belongs in a separate mart
- Use dbt macros and packages (dbt-utils, dbt-date) for repeatable patterns rather than copy-pasting SQL
- Tag models with owners and domains; configure materialisation (table vs. incremental vs. view) per model based on row count and query frequency
- Generate dbt docs as part of CI so column descriptions and lineage graphs are always current
- Write dbt generic tests (not_null, unique, accepted_values, relationships) for every primary key and foreign key in staging and mart layers
- Add custom dbt singular tests for business rules: e.g., revenue rows must have a non-null product_id, refund amount must not exceed original charge
- Set freshness SLA checks in dbt source declarations so stale sources block downstream model runs
- Instrument column-level lineage with dbt artifacts and push to a data catalog (Atlan, DataHub) for discoverability and impact analysis
- Define schema contracts (using tools such as Great Expectations or dbt contract: enforced mode) so that breaking source changes surface before they corrupt gold tables
- Deploy a semantic layer (dbt Semantic Layer / MetricFlow, Cube, or LookML) that maps warehouse columns to business metrics with agreed filters, time grains, and access policies
- Publish certified datasets in Power BI, Tableau, or Looker that point to semantic-layer endpoints rather than raw warehouse tables
- Document every metric in a business glossary: owner, definition, known caveats, last-validated date
- Enable row-level and column-level security in the warehouse and propagate access controls to BI so authorisation is not duplicated in each report
- Set up usage analytics on BI assets to identify stale dashboards, duplicate metrics, and high-impact certified datasets that need SLA monitoring
Ingestion & sync
Raw data enters the platform from operational systems via scheduled batch loads or low-latency change-data capture (CDC). The goal is idempotent, auditable delivery of every source record without mutating the source system.
Storage & modeling (lakehouse)
Raw data lands in object storage or a cloud warehouse bronze layer. From there it is promoted through a medallion architecture into silver (cleansed, typed, deduplicated) and gold (star-schema aggregates ready for analytical queries).
Transformation (dbt)
All in-warehouse transformation is expressed as dbt models: SQL SELECT statements committed to version control, compiled by dbt, and run in dependency order. Business logic lives here — not in BI tools.
Data quality & contracts
Tests are first-class pipeline citizens, not a post-hoc addition. Schema contracts define what the upstream system promises to deliver; data tests verify the warehouse reflects that promise after every load.
Serving & semantic layer / BI
Prepared data surfaces to consumers through a governed semantic layer that centralises metric definitions, and from there into BI tools, notebooks, and operational APIs. No metric definition lives only inside a dashboard.
Engineering principles
These are the convictions that separate pipelines built to last from ones that require an on-call rotation to nurse through every load cycle.
ELT over ETL — load raw, transform in-warehouse
Pre-transforming data in a middleware layer means you can never re-derive history without re-running the extraction. Loading raw first and transforming with dbt inside the warehouse gives you a full audit trail, lets you fix transformation bugs by re-running SQL rather than re-ingesting from source, and leverages the warehouse's distributed compute instead of a single-node server.
Tests are part of the pipeline, not an afterthought
A pipeline without tests is not a pipeline — it is a scheduled guess. Every dbt model should have at minimum a not_null and unique test on its primary key. Freshness checks must block downstream runs when a source goes stale. The cost of writing a test is paid back the first time it catches a silent data corruption that would otherwise have sat in a dashboard for three weeks.
One semantic layer, one definition of a metric
When each BI developer hardcodes metric logic in a workbook, the organisation ends up with five definitions of 'churned customer', none of which agree. A semantic layer forces the definition to be written once, in code, with a named owner. Every downstream consumer — dashboard, notebook, embedded API — queries the same number. Discrepancies become detectable because there is a single source of truth to compare against.
Model for the question, not the source
Source systems are optimised for write throughput, not analytical queries. A mart table should reflect the shape of the question it answers — denormalised enough to avoid repeated joins, at the right grain, with friendly column names — rather than mirroring the source schema. Star schema remains the most readable and query-efficient structure for the majority of analytical workloads.
Idempotency is non-negotiable
Every load and transformation must be safe to re-run without producing duplicate or corrupted data. Idempotent patterns — truncate-and-reload for small tables, merge/upsert with a deterministic key for larger ones, append-only with dedup windows for event streams — eliminate a whole class of production incidents caused by partial failures and retries.
Lineage and ownership must be explicit
Column-level lineage is the map that lets you answer 'if this source column changes, what breaks?' without a two-day audit. Every dbt model should declare a domain owner in its meta block. Every column in a gold-layer table should have a description. When lineage and ownership are implicit, rotating engineers or compliance audits become expensive fire drills.
Reference stack
Tool choices are rationale-driven, not logo-driven. The right answer depends on existing cloud commitment, team SQL fluency, and the volume/latency profile of the workload.
Ingestion & CDC
- AirbyteOpen-core EL platform with 300+ pre-built connectors covering SaaS APIs (Salesforce, HubSpot, Stripe) and databases. Self-hostable on Kubernetes for teams that cannot send data to a vendor SaaS; the connector development kit lets you build proprietary source connectors without forking. Normalisation is optional and deliberately thin — transformation belongs in dbt.
- FivetranFully managed EL with zero-maintenance connectors that automatically handle API version upgrades and schema drift. The right choice when engineering capacity is the bottleneck and the connector catalogue covers your sources; the trade-off is less control over sync scheduling and higher per-row cost at volume.
- Debezium + Apache KafkaThe reference implementation for database CDC: Debezium reads PostgreSQL WAL or MySQL binlog and publishes row-level change events to Kafka topics with sub-second latency. Essential when the source system cannot tolerate batch polling, when you need event ordering guarantees, or when the change stream feeds multiple downstream consumers simultaneously. Carries operational overhead — plan for Schema Registry and Kafka Connect cluster management.
Warehouse & lakehouse
- BigQueryServerless columnar warehouse on GCP with automatic slot scaling, built-in ML functions, and native support for nested/repeated fields that map cleanly to JSON event payloads. The separation of storage and compute removes capacity planning for most workloads; BI Engine provides sub-second cached query results for dashboards. Native integration with Vertex AI makes it the default choice for GCP-committed organisations.
- SnowflakeMulti-cloud warehouse with a virtual warehouse model that lets you size compute independently per workload (ELT jobs on XL warehouses, BI queries on S warehouses). Data sharing across accounts without data movement is its strongest differentiator — useful for sharing certified datasets with external partners or business units on separate Snowflake accounts.
- Delta Lake / Apache IcebergOpen table formats that bring ACID transactions, schema evolution, time-travel, and partition pruning to parquet files on object storage. Delta Lake is the native format on Databricks; Iceberg has broader engine support (Spark, Trino, Flink, DuckDB, BigQuery). Choose these when you need a lakehouse — keeping raw data in object storage while getting warehouse-grade query semantics — or when data must be queryable by multiple engines.
Transformation
- dbt (data build tool)The de facto standard for in-warehouse transformation. Models are plain SQL SELECT statements; dbt handles compilation, dependency resolution, incremental materialisation, and documentation generation. The ref() function creates a DAG that makes column-level lineage computable. dbt tests, sources, and exposures turn the project into a testable, observable artifact rather than a bag of scripts. The separation of concerns it enforces — staging, intermediate, mart — is itself a valuable constraint.
Quality & observability
- dbt tests + dbt source freshnessGeneric tests (not_null, unique, accepted_values, relationships) and singular SQL tests run as part of every dbt job, blocking promotion to gold if they fail. Source freshness declarations let dbt check the max ingestion timestamp against a declared SLA before any downstream model runs — the cheapest form of pipeline monitoring available.
- Great ExpectationsPython-native data quality framework for expectation suites that go beyond what dbt tests express: distribution checks, referential integrity across systems, statistical profiling. Best applied at the ingestion boundary to gate raw data before it enters the bronze layer, or to validate data exchanged across domain boundaries under a schema contract.
- Column-level lineage + data catalog (DataHub / Atlan)dbt artifacts (manifest.json, catalog.json) contain the full column-level lineage graph. Pushing these to a catalog such as DataHub or Atlan makes lineage queryable: 'which dashboards break if we rename this source column?' Ownership metadata and freshness SLA tracking live here too, turning the catalog into the operational runbook for the data platform.
BI & semantic layer
- Power BIMicrosoft-ecosystem BI with deep Azure AD integration, row-level security tied to AAD groups, and DirectQuery / Import hybrid modes. DAX is expressive for time intelligence but becomes a maintenance burden when metric logic diverges across reports — which is the argument for pushing definitions into a semantic layer and connecting Power BI via the XMLA endpoint rather than raw warehouse tables.
- TableauBest-in-class visual analytics with a drag-and-drop authoring model that non-technical stakeholders can use without SQL. Tableau's published data sources act as a weak semantic layer, but lack version control and test coverage — a strong reason to sit a proper semantic layer underneath and connect Tableau to certified virtual tables.
- LookerBI platform built around LookML, a modelling language that is effectively a semantic layer baked into the tool. Every metric, dimension, and join is defined once in version-controlled LookML files; every query Looker generates is traceable to those definitions. The right choice when the organisation is willing to invest in LookML authorship and wants the semantic layer and BI surface to be a single, tightly integrated system.
- MetricFlow / dbt Semantic LayerOpen semantic layer framework, now part of dbt Core. Metric definitions written in YAML sit alongside dbt models and compile to warehouse SQL at query time, making metrics queryable from any connected BI tool via the same interface. Avoids vendor lock-in to a single BI platform while guaranteeing definitional consistency — the same metric SQL runs whether the consumer is Power BI, a Python notebook, or a Slack-native analytics bot.
How we'd choose
There's rarely one right answer — these are the trade-offs we weigh before recommending an approach.
ELT vs ETL
The question is not which is newer — it is where your transformation logic should live and what happens when that logic changes.
| Criterion | ELT | ETL |
|---|---|---|
| Where logic lives | In the warehouse as dbt SQL — version-controlled, testable, diffable | In a middleware server or custom script — often untested, harder to diff |
| Raw data preservation | Raw layer always present; bugs fixed by re-running SQL without re-extraction | Raw data discarded after transform; bug fixes require full re-extraction |
| Compute model | Warehouse elastic compute scales with data volume automatically | Middleware server must be sized for peak load; over-provisioned at rest |
| Schema evolution | New source columns land in raw automatically; downstream models opt in explicitly | Pipeline must be redeployed to pass through new columns; source and sink schemas tightly coupled |
| Best fit | Cloud warehouse or lakehouse already in place; team has SQL fluency; audit trail required | Data must be masked or reduced before leaving the source network (compliance, PII egress constraints) |
Warehouse vs Lakehouse vs Data Lake
Each architecture optimises for a different balance of query performance, storage cost, and schema flexibility. Most production platforms end up combining two of the three.
| Criterion | Cloud Warehouse | Lakehouse | Data Lake |
|---|---|---|---|
| Primary storage format | Proprietary columnar (BigQuery Capacitor, Snowflake FDN) | Open format parquet with Delta/Iceberg table layer on object storage | Raw files in object storage (parquet, JSON, Avro) — no table layer |
| ACID transactions | Native; row-level updates and deletes fully supported | Supported via Delta/Iceberg transaction log; compaction needed to maintain read performance | Not supported natively; upserts require full partition rewrites |
| Query performance | Highest for structured SQL; automated clustering and caching | Near-warehouse for optimised tables; degrades if files are not compacted | Variable; depends entirely on query engine (Athena, Trino) and partition layout |
| Storage cost | Higher — vendor-managed storage with markup over raw object storage | Low — data lives in your object storage bucket at commodity rates | Lowest — no compute layer overhead, no table format overhead |
| Schema flexibility | Schema-on-write; DDL changes required before new columns land | Schema evolution via Delta/Iceberg; time-travel lets you query old schemas | Schema-on-read; any structure accepted at write time, interpreted at query time |
| Typical use case | Gold-layer analytical tables, BI serving, sub-second dashboard queries | Unified platform for both ML feature engineering (semi-structured) and BI (star schema gold tables) | Long-term archival, ML training data at scale, raw event replay without schema commitment |
Anti-patterns to avoid
These are the failure modes that appear repeatedly in data platforms that were built quickly without architectural guardrails.
Spreadsheets as a database / no single source of truth
When each team maintains its own revenue tracker, headcount sheet, or customer list in Excel or Google Sheets, the organisation has as many versions of reality as it has spreadsheets. Reconciling the CFO's number with the ops dashboard becomes a quarterly fire drill. Spreadsheets have no access control audit trail, no schema enforcement, no lineage, and no automated freshness check — which means errors compound silently.
Establish a single authoritative source for each business entity in the warehouse (e.g., the CRM is the system of record for customer data; the ERP is the source of record for invoices). Ingest those systems via CDC or scheduled EL into a bronze layer, transform to certified gold tables in dbt, and redirect all reporting to query those tables. Spreadsheets are permitted as a consumption surface, not a storage layer.
Transformations buried in BI tools
When analysts build calculated columns, custom SQL joins, and metric logic directly inside Tableau workbooks, Power BI Desktop files, or Looker explores that bypass LookML, the definitions become invisible to the data team and untestable. Two dashboards will inevitably compute the same metric differently because neither developer knew the other had already built it. These embedded transformations have no version control, no CI, and no lineage — when a source column is renamed, the impact is discovered only when an executive's number changes.
Business logic belongs in dbt models or the semantic layer — period. BI tools should connect to certified, already-transformed mart tables or semantic layer endpoints. If a calculated field cannot be expressed without joining or filtering in the BI tool, that is a signal the mart model is incomplete, not that the BI tool is the right place to fix it. Enforce this by publishing certified data sources and discouraging direct connections to raw or silver tables from BI tools.
No data tests — silent breakage
Pipelines without tests appear to work until they do not. A source team renames a column, a CDC connector silently stops emitting deletes, or a timezone bug causes duplicate event rows — and the first signal is a confused stakeholder asking why last month's revenue is 12% lower than what was reported in the board deck. Without not_null, unique, and referential integrity tests in dbt, and without freshness SLA checks on sources, the pipeline has no way to detect or surface these failures before they reach dashboards.
Add dbt generic tests to every staging model's primary key (not_null + unique at minimum) before merging any new model to main. Declare freshness SLAs on every dbt source block and configure your orchestrator to send alerts when freshness checks fail. For critical gold-layer tables, add custom singular tests that encode business invariants — e.g., daily revenue must be positive, refund rows must reference a valid order. Treat a test failure in CI as a pipeline failure, not an optional warning.
Go deeper
Data & Analyticscovers a lot of ground. Here are the specific things we're most often asked to build.
Common questions
We only have spreadsheets — is that enough to start?
Almost always, yes. Most projects begin with messy exports. We turn them into a clean, documented model first, then decide together whether a fully automated pipeline is worth the investment.
Will the dashboard update by itself?
If you want it to. We can deliver a one-off analysis or set up scheduled pipeline refreshes so the numbers stay current with no manual work from your team.
Other services
Have something in mind?
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