> Markdown version of [/jobs/ext/4162384-data-engineer](https://www.wearedevelopers.com/jobs/ext/4162384-data-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 Engineer - **Company:** S2K IT Solutions - **Location:** Wokingham, UK - **Salary:** £39,000 - £51,000 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Amazon Web Services, Microsoft Azure, Bash Shell, Big Data, Cloud Computing, Cloud Storage, Computer Programming, Databases, Extract Transform Load (ETL), Data Warehousing, Relational Databases, Database Design, Apache Hadoop, Python (Programming Language), Unix Shell, Microsoft SQL Server, Oracle (Applications), SQL Databases, Data Streaming, Data Processing, Scripting, Informatica Powercenter, Apache Spark, Vba Programming Language, Data Pipelines - **Published:** October 14, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=956f232b39876581 ## About the Role * Cloud Platforms: Strong experience using AWS or Azure services for storing and processing data. * Coding & Scripting: High proficiency in Java for building scalable apps, and Python or Bash (Unix shell) for automation tasks. * Database Design: Deep knowledge of relational databases like Oracle and Microsoft SQL Server, including how to structure tables efficiently. * Data Warehousing & ETL: Solid understanding of data warehouse concepts and hands-on experience using ETL tools like Informatica to move and clean data. * Analytical Thinking: Excellent problem-solving skills to understand complex data and turn it into useful business insights. ## Description * Build Data Pipelines: Design, develop, and optimize data streams using tools like Apache Spark, Hadoop, and Informatica to process massive datasets efficiently. * Manage Data Warehouses: Build and maintain central data warehouses using Oracle, Microsoft SQL Server, and SQL to support business reports and dashboards. * Collaborate with Teams: Work closely with different departments to understand what data they need and turn those needs into practical technical setups. * Ensure Database Quality: Use database design best practices to keep data accurate, secure, and running fast across all platforms. * Automate Workflows: Write automated scripts using Bash, Python, Java, or VBA to speed up daily data processing and remove manual work. * Manage Cloud Storage: Organize cloud storage on GCP, ensuring team access is highly secure and system performance is optimized. * Analyze Datasets: Study large data profiles to spot trends, find system bugs (anomalies), and suggest ways to improve processes.