> Markdown version of [/jobs/ext/4596020-data-ingestion-engineer](https://www.wearedevelopers.com/jobs/ext/4596020-data-ingestion-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Ingestion Engineer

- **Company:** Focus Cloud Limited
- **Location:** London, United Kingdom
- **Salary:** £156,000 - £169,000
- **Contract:** Permanent contract
- **Skills:** Big Data, Extract Transform Load (ETL), Software Debugging, Distributed Computing Environment, Python (Programming Language), Queue Management Systems, Data Streaming, Data Ingestion, Apache Spark, Pyspark
- **Published:** November 6, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5955042840

## About the Role

* Strong production experience with PySpark / Apache Spark
 * Strong Python engineering skills
 * Proven experience building, debugging or operating large-scale data ingestion or ETL pipelines
 * Experience with very large datasets, ideally at PB scale or in similarly high-throughput environments
 * Comfort with messy, corrupt, incomplete or inconsistent data across complex data domains
 * Experience with distributed data processing and debugging production pipeline failures
 * Understanding of orchestration across multi-step pipelines and downstream dependencies
 * A practical, delivery-focused mindset and the ability to ramp up quickly with minimal hand-holding

## Description

* Data ingestion: Build, run and harden high-volume batch ingestion pipelines. Diagnose and fix failing or blocked jobs, and make pipelines resilient so one bad segment can't block wider workflows.
 * PySpark: Optimise Spark jobs for throughput, compute efficiency and cost across petabyte-scale datasets.
 * Python engineering: Write clean, production-grade Python for ingestion logic, validation, tooling and automation that reduces manual intervention.
 * Complex data domains: Work with varied data formats from partners and suppliers. Investigate corrupt, malformed or unexpected data and build robust handling for it.
 * Orchestration: Support multi-step workflows, including dependencies, retries, queue management and prioritisation of critical datasets.
 * Collaboration: Work alongside permanent engineers to unblock important data flows while longer-term platform improvements are built.