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Data Engineering
— pipelines you can trust

Resilient, observable data pipelines — batch and streaming, declarative transformations, data contracts and infrastructure as code — delivering trusted data for analytics and AI.

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Data Engineering

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Topics we cover

Data engineeringData pipelinesETL / ELTReal-time streamingChange data capture (CDC)Apache KafkaApache SparkdbtData contractsData qualityData observabilityDataOpsOrchestration (Airflow, Fabric pipelines)Infrastructure as CodeUnstructured data pipelinesVector embedding pipelinesAI-assisted data engineering

Status quo · late 2026

What has changed — and why it matters now

Streaming became ordinary

CDC and event streaming feed lakehouses in near-real time; batch is a choice, not a limit.

Contracts and observability

Data contracts between producers and consumers, plus observability for freshness, volume, schema and distribution, replace after-the-fact firefighting.

Pipelines for AI

Document parsing, chunking and embedding pipelines are now data engineering — with lineage and access control preserved into vector stores.

How we approach it

Our method for Data Engineering

01Source & ingestConnectors, CDC, APIs, files, events; schema handling; replay.

02TransformDeclarative SQL/dbt or Spark; tests; incremental models; semantic layer.

03OrchestrateDependency-aware scheduling, retries, SLAs, alerts.

04Observe & governQuality checks, lineage, contracts, cost tracking.

05DeployIaC, CI/CD, environments, code review — DataOps.

In depth

Pipeline patterns

Batch ELT

Land raw, transform in-warehouse with dbt / Spark, incremental loads.

Streaming

Kafka / Event Hubs / Kinesis / Pub/Sub → stream processing → lakehouse tables.

CDC

Debezium / native CDC from operational databases with exactly-once semantics.

Unstructured & embeddings

Parse, chunk, embed, index — with metadata and permissions.

Reverse ETL

Push curated data back into operational and SaaS systems.

What you get

Deliverables

Ingestion frameworksReal-time streamingTransformation logicOrchestrationIaC deploymentsPipeline observability

Technology-agnostic

Platforms we work across

We design for the outcome first and choose the platform second — across Microsoft, AWS and Google Cloud, and every major AI provider.

Microsoft Fabric Data Factory & SparkDatabricksAWS Glue / Kinesis / MSKGoogle Dataflow / Pub/SubApache KafkadbtAirflowTerraform

More Data services

Data Lake Architecture Data Analytics Data Science Data Governance Data Mesh, Fabric & API Access

Ready to talk about Data Engineering?

One email reaches the architects and engineers who will do the work. We reply within one working day.

info@uspc.co.uk Contact page

Data Engineering— pipelines you can trust