Data Engineering
Lakehouse architecture, data contracts, governance and quality at scale.
Data engineering is the foundation on which every analytics and AI initiative rests. Without reliable, well-modelled, governed data flowing through trustworthy pipelines, every downstream intelligence initiative fails — silently or loudly. TDS Global builds data engineering platforms that organisations can build on with confidence.
How we approach data engineering.
Bad data engineering doesn't fail spectacularly — it fails gradually: dashboards that conflict, AI models trained on stale data, analysts who distrust their own reports. We address this by designing data systems with quality and trust as first-class engineering concerns: schema contracts enforced at ingestion, data quality checks embedded in pipelines, lineage tracked end-to-end and SLAs defined and monitored for every dataset that matters.
Specific deliverables within this capability.
Data Lakehouse Architecture
Databricks, Snowflake or open-source (Apache Iceberg, Delta Lake) lakehouse platforms designed for unified batch and streaming workloads serving both analytics and AI.
ELT/ETL Pipeline Engineering
dbt-based transformation layers, Apache Airflow orchestration and Fivetran/Airbyte ingestion connectors — built for reliability, observability and testability.
Streaming Data Pipelines
Apache Kafka, Confluent and AWS Kinesis-based real-time ingestion pipelines with exactly-once semantics and dead letter queue handling.
Data Contracts
Schema registries, producer-consumer contracts and breaking change governance that prevent silent data corruption downstream.
Data Quality & Observability
Great Expectations, Monte Carlo and custom quality checks embedded in every pipeline, with anomaly detection and SLA alerting.
Data Governance
Data catalogues (Datahub, Alation), lineage tracking, PII classification, access control and retention policy automation.
How we execute every data engineering engagement.
Domain Modelling
We design the conceptual data model and domain boundaries before building ingestion pipelines — schema decisions made deliberately, not discovered later.
Ingestion Layer
Raw data lands in a structured, immutable landing zone. No transformation in transit. Full audit trail from source to raw.
Transformation Layer
dbt models transform raw data through staging, intermediate and mart layers — fully tested, version-controlled and documented.
Quality Gates
Quality checks run at every layer. Pipelines fail loudly and alerting fires before downstream consumers see corrupted data.
Serving Layer
Curated, governed datasets are served to analysts, dashboards and AI systems through defined access patterns with performance guarantees.
How you know we do this well.
These are the specific engineering practices and standards that distinguish our work — not claims, but verifiable commitments baked into every engagement.
We write dbt tests before building transformations — data quality is enforced by pipeline, not monitored after the fact
Our data pipelines are version-controlled, reviewed in pull requests and deployed through CI/CD — like application code
We implement data contracts that catch breaking schema changes before they reach production consumers
Every pipeline we build has defined SLAs, freshness checks and latency alerting — not just success/failure monitoring
Our lakehouse architectures separate storage, compute and governance — no single-vendor lock-in by design
We model data using Kimball and Inmon principles, selecting the appropriate paradigm based on access patterns, not convention
What gets delivered.
Measurable engineering outcomes our practice delivers consistently across client engagements.
Single, trusted data platform replacing 10–30 disconnected data sources
Pipeline reliability >99.5% with automated quality gates and alerting
Data freshness SLAs defined and monitored for every business-critical dataset
AI and ML models trained on clean, governed, lineage-tracked data
Data governance framework satisfying regulatory audit requirements
Tools & platforms we use.
Bring Data Engineering capability into your organisation.
Our practice leads are available to discuss your specific technical challenges and what a scoped engagement would look like.