Database Developer

About Zinkworks Zinkworks partners with leading Telecommunications and Financial Services organizations to modernize legacy systems, migrate mission-critical platforms to the cloud, and engineer AI-driven automation. From OSS transformation to rApp development and network intelligence, our teams simplify complexity and turn it into competitive advantage. Based in Ireland and operating across the EU, UK, and US, Zinkworks combines deep domain expertise with delivery excellence to help clients modernize faster and operate smarter. About the Role Zinkworks is seeking an experienced Data Engineer to design, develop, enhance, and support scalable cloud-based data solutions. This is a hands-on engineering role suited to someone with a strong foundation in SQL, Python, database development, data modelling, and ETL/ELT pipeline delivery. You will work with established cloud data platforms, extending existing solutions and developing the pipelines, transformations, integrations, and data models needed to support operational, analytical, and AI-driven use cases. The role will involve working with relational, NoSQL, and graph-oriented data models. Previous experience with Google Cloud Spanner or graph technologies would be beneficial, but it is not essential. We are particularly interested in strong data engineers who can apply sound engineering principles, work effectively with complex datasets, and quickly learn new platforms and technologies. Key Responsibilities - Design, develop, test, and maintain reliable and scalable ETL/ELT data pipelines using Apache Airflow . - Develop efficient SQL queries, stored logic, and Python-based data-processing solutions. - Work with existing cloud data platforms and extend their schemas, integrations, transformations, and querying capabilities. - Build and maintain data solutions using cloud technologies such as: - Google Cloud Dataflow, Dataform or dbt, BigQuery, and Cloud Spanner - Azure Data Factory, Synapse Analytics, and Databricks - AWS Glue and Lambda - Design and implement relational, dimensional, star-schema, NoSQL, and, where appropriate, graph-oriented data models. - Process and integrate data from structured, semi-structured, and unstructured sources. - Profile source data, assess data quality, identify patterns and anomalies, and use these findings to inform data-model and pipeline design. - Implement data-quality controls, reconciliation processes, automated validation, and pipeline testing. - Monitor data pipelines and troubleshoot issues relating to performance, reliability, scalability, and data integrity. - Collaborate with software engineers, architects, business analysts, testers, product teams, and client stakeholders to translate requirements into practical data solutions. - Contribute to technical design discussions and provide recommendations on data architecture, storage, transformation, and integration approaches. - Manage source code through Git and support automated build, test, and deployment processes using CI/CD and DevOps practices. - Apply appropriate security, access-control, data-governance, and compliance standards. - Produce and maintain clear technical documentation covering data models, pipelines, transformations, dependencies, and operational procedures. - Participate fully in agile delivery activities, including planning, estimation, technical reviews, and continuous improvement. Required Technical Skills and Experience - Strong proficiency in SQL and hands-on experience with database development. - Experience working with both relational and NoSQL databases . - Strong experience using Python for data engineering, transformation, automation, or integration. - Proven experience designing, developing, and supporting ETL/ELT data pipelines using Apache Airflow . - Hands-on experience with data-engineering services within at least one major cloud platform, such as: - Google Cloud Platform: Dataflow, Dataform or dbt, BigQuery - Microsoft Azure: Azure Data Factory, Synapse Analytics, Databricks - <