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Service 04 — Data Engineering

Data pipelines you can depend on.

We design and build the ingestion, transformation and orchestration that move data reliably from operational systems into analytics, reporting and AI.
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The business problem

Reporting and AI are only as reliable as the data underneath.

When data is pulled by hand from multiple systems, reconciled in spreadsheets and refreshed whenever someone has time, every report carries a question mark. Scaling analytics or AI on that foundation multiplies the problem.

Data engineering replaces fragile manual steps with automated, tested and monitored pipelines, so that the numbers are current, consistent and explainable.

Signals this might be you

  • 01Monthly reports take days of manual preparation
  • 02Different teams produce different versions of the same metric
  • 03Nobody is sure when a dataset was last refreshed
  • 04AI projects are blocked waiting for clean data

What we provide

What LT Lab provides

Modern, code-based data pipelines designed around your sources, volumes, latency needs and budget.

  1. 01

    Ingestion

    Connectors for APIs, databases, ERPs, CRMs, files and event streams — batch or near real-time.

  2. 02

    Transformation

    Tested, version-controlled SQL and Python models that turn raw data into consistent business entities.

  3. 03

    Orchestration

    Scheduled and dependency-aware workflows with retries, backfills and clear ownership.

  4. 04

    Data quality and observability

    Freshness checks, validation rules, lineage and alerting so issues are caught before they reach a dashboard.

Capabilities

What this service covers

  • ETL
  • ELT
  • Data pipelines
  • Orchestration
  • Data quality
  • Data ingestion
  • API pipelines
  • Streaming
  • Batch processing
  • Cloud data infrastructure

Typical architecture

A reference data pipeline

Each stage is automated, tested and monitored, and can be scaled independently.

  1. 01

    Sources

    Operational systems and external feeds

    • APIs
    • ERP
    • CRM
    • Files
    • Databases
  2. 02

    Ingestion

    Extract and land raw data

    • Connectors
    • CDC
    • Streaming
    • Batch loads
  3. 03

    Transformation

    Clean, model and test

    • dbt
    • SQL
    • Python
    • Data tests
  4. 04

    Storage

    Governed analytical storage

    • Warehouse
    • Lakehouse
    • PostgreSQL
  5. 05

    Consumption

    Where value is created

    • BI dashboards
    • AI models
    • Reverse ETL
    • APIs
Data flows through Sources, then Ingestion, then Transformation, then Storage, then Consumption.

Example use cases

Where this creates value

01

Automated management reporting

Scheduled pipelines consolidate finance, sales and operations data so dashboards refresh without manual work.

Outcome: Reports available on schedule, every time

02

API data consolidation

Data from SaaS platforms, payment providers and partner APIs is ingested into a single analytical store.

Outcome: A complete view across previously separate tools

03

Data foundation for AI

Curated, documented datasets prepared for machine learning, forecasting and retrieval-augmented AI.

Outcome: AI initiatives start from trusted data

04

Legacy migration

Moving data from legacy databases and spreadsheets into modern, maintainable cloud infrastructure.

Outcome: Lower maintenance and better performance

Technologies

Chosen for your environment, not our preferences.

Languages
Python / SQL
Pipelines
Airflow / dbt / Kafka / Custom connectors
Storage
PostgreSQL / BigQuery / Snowflake / Azure Synapse
Cloud
AWS / Azure / Google Cloud / Docker

Engagement process

How an engagement runs.

  1. 01 —

    Understand

    We map the workflows, systems, data and business objectives.

    • + Stakeholder workshops
    • + Process and system mapping
    • + Data assessment

    Problem definition and priorities

  2. 02 —

    Architect

    We design a technical approach around the organization's existing environment and future requirements.

    • + Solution architecture
    • + Technology selection
    • + Delivery plan

    Architecture and roadmap

  3. 03 —

    Build

    Our engineers develop, integrate and test the solution iteratively.

    • + Iterative sprints
    • + Integration and testing
    • + Regular demos

    Working, tested software

  4. 04 —

    Improve

    We deploy, monitor and continue optimizing the system as the organization grows.

    • + Deployment
    • + Monitoring and support
    • + Continuous improvement

    A system that keeps getting better

Related services

Start a conversation

Let's talk about Data Engineering.

Tell us about the problem, the systems involved and where you want to be. We'll help you work out the right first step.

Tell us about your projectconsulting@ltlabsolutions.io