Open to full-time and contract roles

Building dependable cloud platforms and data systems.

I’m Elijah Campbell, a Data and Cloud Engineer designing scalable pipelines, distributed processing systems, cloud-native infrastructure, and analytics-ready platforms.

Explore my work Download Resume
30%Pipeline latency reduction
40%Query performance improvement
£10KQuarterly infrastructure savings
3Major cloud platforms

01 / About

Engineering systems that remain useful after deployment.

My work connects cloud infrastructure, data engineering, backend APIs, and operational reliability. I design for performance, data quality, observability, cost efficiency, and maintainability—not just successful demos.

Cloud PlatformsAWS, Azure, GCP, Terraform, Docker, CI/CD
Data PipelinesSpark, PySpark, Airflow, Kafka, dbt, ETL/ELT
Data PlatformsSnowflake, Redshift, BigQuery, PostgreSQL
OperationsTesting, monitoring, data quality, performance tuning

02 / Selected Work

Technical projects presented as engineering case studies.

Featured · Data Platform

Project RISING

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A climate-driven disease burden platform for ASEAN health resilience, combining validated public-health datasets, ETL foundations, analytical processing, and a FastAPI intelligence service.

Challenge

Fragmented health and climate datasets made reliable analysis and early-warning workflows difficult.

Engineering response

Created a modular ingestion, validation, transformation, intelligence, and API architecture with operational safeguards.

Reliability

Added schema validation, deduplication, retry and DLQ concepts, CI workflows, clearer errors, and architecture documentation.

PythonFastAPIETLREST APIGitHub ActionsData Validation

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03 / Architecture

A modern data platform, from ingestion to trusted delivery.

01

Design for failure

Retries, validation, idempotency, monitoring, and recovery are part of the architecture.

02

Automate the platform

Infrastructure as code and CI/CD make environments repeatable, reviewable, and safer.

03

Measure the outcome

Performance, cost, reliability, and data quality are engineering outputs—not afterthoughts.

04 / Experience

Hands-on delivery across data and cloud systems.

May 2026 — Present
Amdari · Manchester, UK

Data Engineer Intern

Led scalable cloud data-pipeline development, supported storage and infrastructure optimization, improved production query performance, and contributed to distributed processing and real-time analytics initiatives.

  • Reduced pipeline processing latency by 30%.
  • Improved database and query performance by 40%.
  • Helped reduce infrastructure costs by £10,000 per quarter.
2026
Global Hackathon

Lead Data Pipeline Engineer · Project RISING

Owned repository structure, pipeline architecture, ETL foundations, dataset integration, API support, documentation, CI, and reliability improvements for a public-health data platform.

05 / Technology

Tools selected around real platform responsibilities.

Languages & APIs

Python · SQL · Java · Bash · FastAPI · Flask · REST

Data Engineering

Spark · Airflow · Kafka · dbt · Kinesis · ETL/ELT · Dimensional Modeling

Cloud & Infrastructure

AWS · Azure · GCP · Terraform · Docker

Warehousing & Storage

Snowflake · Redshift · BigQuery · PostgreSQL · MySQL · MongoDB

Quality & Observability

Pytest · Data Validation · Datadog · Prometheus · Grafana

Delivery

Git · GitHub Actions · CI/CD · Agile · Architecture Documentation · Stakeholder Collaboration

Let’s work together

Need a dependable Data or Cloud engineer?

I’m open to full-time and contract Cloud & Data Engineering roles.

campbell_salvus@hotmail.com LinkedIn GitHub