pipeline.build.ts 01exportfunctiontransform() { 02 build.test() 03 ci.run('pass') 04 return shipped 05} BUILD PASSED · v2.14.0 SPRINT 01 RELEASE
Data pipelines & data engineering · serving businesses worldwide since 2009

Data pipelines that get the right data to the right system, reliably and on time.

Need a data pipeline, ETL/ELT rebuild or analytics-ready warehouse? Ask Milo - he’ll help you with your queries.

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Data pipelines and integrations built into production since 2009
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Average reduction in manual data reconciliation after go-live
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Contracted pipeline uptime SLA on managed data platforms
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Source systems typically unified into a single pipeline
Spreadsheets vs. scripts vs. a real pipeline

When a proper data pipeline becomes the better choice

Reports depend on someone manually exporting and merging spreadsheets

Numbers change depending on who ran the export and when, and nobody fully trusts the dashboard anymore.

Your systems all hold a different version of the truth

Customer, order and finance data drift apart because nothing keeps them reliably in sync.

A single broken script can silently corrupt a week of data

Without monitoring, validation or alerting, pipeline failures go unnoticed until someone downstream asks why the numbers look wrong.

Your data team spends more time firefighting pipelines than building anything new

Engineering time goes into patching brittle scripts instead of new data products, models or analytics.

ORCHESTRATOR SOURCE APIs INGESTION TRANSFORM WAREHOUSE BI / DASHBOARDS PIPED, NOT PATCHED Schema-aware · Monitored · Built to scale
Data pipeline & data engineering services from strategy to support

One team for data strategy, pipeline engineering, warehousing and ongoing data reliability.

Our data pipeline services cover the full lifecycle: data strategy and audit, source and schema mapping, ETL/ELT pipeline development, warehouse and lake design, orchestration, monitoring and post-launch support. One Milleniance team remains accountable from the first audit through production.

Discipline 01

Data strategy & source audit

Auditing existing source systems, data quality and reporting needs, and defining a realistic architecture and migration roadmap before implementation begins.
Source system & data quality auditSchema & data model mappingData architecture & tooling selectionPipeline roadmap & cost estimation

Our audit work can include profiling data quality issues at the source, mapping dependencies between systems, and sequencing which pipelines to build first for the fastest reporting impact.

Discipline 02

Pipeline & warehouse architecture

Designing ETL/ELT pipelines, data warehouses and lakes around your actual reporting, analytics and operational needs rather than a generic reference architecture.
ETL / ELT pipeline designData warehouse & lake architectureBatch & real-time / streaming designData governance & access design

Architecture decisions account for data volume, latency requirements and who consumes the data, so the pipeline is sized for your business rather than over- or under-built.

Discipline 03

Pipeline & integration engineering

Building the pipelines, connectors and transformations that move and reshape data from source systems into analytics-ready tables reliably.
Pipeline development (Airflow, dbt, Spark)CRM / ERP / API source integrationsData transformation & modellingChange data capture & incremental loads

Engineering includes data validation, idempotent loads and version-controlled transformations, so pipelines are testable and safe to change as source systems evolve.

Discipline 04

Orchestration, monitoring & data operations

Scheduling, monitoring, alerting and incident response keep pipelines reliable once they are running against production data and real deadlines.
Pipeline orchestration & schedulingData quality monitoring & alertingCost & performance optimisationIncident response & on-call runbooks

Operations work covers freshness checks, anomaly detection and a defined process for what happens when a pipeline fails, so a broken feed gets caught before a report goes out wrong.

Data pipeline and data engineering technologies

We choose the data stack around your source systems, data volume and reporting requirements.

Our data engineering stack includes modern orchestration, transformation, warehousing and BI technologies. Where your business already has a working stack, we work within it by default and recommend migration only when the benefits are clear.

PythonAirflowdbtApache SparkKafkaSnowflakeBigQueryRedshiftPostgreSQLMongoDBFivetranSegmentAWS GlueDatabricksS3Azure Data FactoryLookerPower BITableauTerraformDockerKubernetes
Data pipeline development process

A defined process from source audit to pipeline launch and ongoing data operations.

01

Discovery, source audit & data mapping

We map source systems, data quality, volumes and reporting needs before defining the pipeline architecture and build sequence.

02

Senior data engineering team assignment

A named technical lead and data engineers stay accountable throughout, from architecture through pipeline operations.

03

Pipeline development, integration & launch

Pipeline engineering, transformations and QA work against one delivery roadmap, with pipelines released and validated incrementally.

04

Post-launch monitoring, scaling & optimisation

After launch, we support monitoring, cost optimisation, schema changes and new pipeline development as data sources and reporting needs grow.

Selected work

A handful of the platforms and campaigns we've shipped recently.

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Frequently asked

What our clients usually ask before the first call.

A data pipeline automatically moves, transforms and validates data between source systems, such as your CRM, ERP or product database, and the places it’s needed, such as a warehouse or dashboard. It is worth building once manual exports and spreadsheet merges start producing unreliable or inconsistent numbers.

A one-off API integration usually moves data between two specific systems for a specific purpose. A data pipeline is a broader, monitored process that extracts data from multiple sources, transforms and validates it, and loads it into a destination like a warehouse on a reliable schedule, with error handling built in.

It depends on the number of source systems, data volume and transformation complexity. Most Milleniance data engagements aim for a working, validated pipeline for the first priority data sources in 6 to 10 weeks after discovery and scope are agreed.

Yes. We build pipelines to feed your existing warehouse and BI tools wherever practical, and only recommend a change of tooling when there’s a concrete performance, cost or scaling reason to do so.

Yes. Post-launch support can include monitoring, alerting, schema-change handling, performance optimisation and new pipeline development, with continuity from the team that built it.
From the practice

Data engineering insights: pipelines, warehousing, orchestration and data reliability.

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Let's see if we're the right fit

Thirty minutes with a senior data engineer, before either of us commits to anything.

No sales deck, no discovery call with someone who hands you off afterwards. Scoped around your actual data sources, volume and reporting needs. We respond within one business day.

Talk to a Senior Data Engineer →
Talk to a Senior Data Engineer →