Data Engineer_Freelancing_Remote Opportunity

Role & responsibilities

Data Pipeline Engineering

Design, develop, and maintain robust ETL/ELT data pipelines to ingest, transform, and load

data from multiple sources.

Build and optimise data models and schemas within cloud-based data warehouses and data

lakes.

Support the integration of third-party data sources and APIs into the data platform.

Contribute to orchestration and transformation workflows that support reliable reporting and

insight generation.

API & Data Application Development

Develop and contribute to FastAPI repositories to add functionality to existing APIs.

Build lightweight data applications and internal tools using FastAPI, Streamlit, and the broader

Python ecosystem.

Apply REST API design principles, async programming concepts, dependency injection, and

OpenAPI/Swagger documentation practices.

Data Modelling & Platform Optimisation

Apply dimensional modelling techniques, including star and snowflake schemas, while

understanding normalisation and denormalisation trade-offs.

Work across relational databases, cloud data warehouses, and non-relational systems, with

an understanding of when to apply each.

Optimise queries, schemas, and data workflows to support analytical and operational

workloads.

Data Quality, Governance & Security

Implement data quality checks, monitoring, and alerting to ensure the integrity and reliability

of data assets.

Work with data governance and security teams to ensure compliance with data protection

regulations, including GDPR.

Support data lineage, cataloguing, access control, and observability practices across the data

estate.

Software Engineering & Delivery Practices

Apply Git-based software engineering workflows, including branching strategies, pull request

reviews, CI/CD pipelines, automated testing, and code quality tooling.

Develop modular, testable, production-grade Python code with appropriate use of libraries

and frameworks.

Support containerisation and deployment of Python services and data workloads into

production environments.

Stakeholder Collaboration & Documentation

Collaborate with analytics, data science, product, engineering, and business teams to

understand requirements and deliver practical data solutions.

Develop and maintain documentation for data pipelines, architecture, processes, and

technical decisions.

Communicate technical concepts clearly to non-technical stakeholders and take ownership of

problems end-to-end