Asks for

The ad says“Amazon Web Services: AWS Glue, Lambda”
The ad says“Proven experience designing, developing, and supporting ETL/ELT data pipelines using Apache Airflow.”
The ad says“Amazon Web Services: AWS Glue, Lambda”
The ad says“Microsoft Azure: Azure Data Factory, Synapse Analytics, Databricks”
The ad says“Google Cloud Platform: Dataflow, Dataform or dbt, BigQuery”
The ad says“Experience with Git, CI/CD, and DevOps delivery practices.”
The ad says“Strong understanding of database and data-modelling principles, including relational models, dimensional models, and star-schema structures.”
The ad says“Strong proficiency in SQL and hands-on experience with database development.”
The ad says“Microsoft Azure: Azure Data Factory, Synapse Analytics, Databricks”
The ad says“Google Cloud Platform: Dataflow, Dataform or dbt, BigQuery”
The ad says“Google Cloud Platform: Dataflow, Dataform or dbt, BigQuery”
The ad says“Google Cloud Platform: Dataflow, Dataform or dbt, BigQuery”
The ad says“Experience with Git, CI/CD, and DevOps delivery practices.”
The ad says“Proven experience designing, developing, and supporting ETL/ELT data pipelines using Apache Airflow.”
The ad says“Experience with Git, CI/CD, and DevOps delivery practices.”
The ad says“Hands-on experience with data-engineering services within at least one major cloud platform, such as:”
The ad says“Experience working with graph databases or graph-query technologies such as Neo4j, Gremlin, or Spanner Graph.”
The ad says“Experience working with graph databases or graph-query technologies such as Neo4j, Gremlin, or Spanner Graph.”
The ad says“Amazon Web Services: AWS Glue, Lambda”
The ad says“Microsoft Azure: Azure Data Factory, Synapse Analytics, Databricks”
The ad says“Experience working with graph databases or graph-query technologies such as Neo4j, Gremlin, or Spanner Graph.”
The ad says“Experience working with both relational and NoSQL databases.”
The ad says“Strong experience using Python for data engineering, transformation, automation, or integration.”
The ad says“Experience working with both relational and NoSQL databases.”
The ad says“Experience working with graph databases or graph-query technologies such as Neo4j, Gremlin, or Spanner Graph.”
The ad says“Strong proficiency in SQL and hands-on experience with database development.”
The ad says“Microsoft Azure: Azure Data Factory, Synapse Analytics, Databricks”

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
- Amazon Web Services: AWS Glue, Lambda
Strong understanding of database and data-modelling principles, including relational models, dimensional models, and star-schema structures.
Experience working with graph databases or graph-query technologies such as Neo4j, Gremlin, or Spanner Graph.
Experience working with structured, semi-structured, and unstructured data.
Strong data-analysis and profiling skills, including the ability to assess data quality, investigate anomalies, and identify patterns.
Experience validating data transformations and testing data pipelines using both automated and manual approaches.
Experience with Git, CI/CD, and DevOps delivery practices.
Understanding of data security, access control, governance, and regulatory or compliance requirements.
Ability to troubleshoot complex data issues and improve the performance, reliability, and maintainability of data solutions.
Demonstrated ability to learn unfamiliar data platforms and technologies quickly.

Desirable Technical Skills and Experience

Hands-on data-engineering experience within Google Cloud Platform.
Experience with Google Cloud Spanner, including querying, schema extension, integration, or its graph capabilities.
Familiarity with Docker and Kubernetes.
Experience with real-time data processing, streaming platforms, or event-driven architectures.
Telecommunications industry experience, particularly involving network, operational, or OSS data.
Experience supporting large-scale or distributed data platforms.
Familiarity with data lineage, observability, metadata management, and automated data-quality frameworks.
Experience developing AI agents or integrating data platforms with agent-based systems.
Familiarity with integration frameworks and protocols such as the Model Context Protocol (MCP).

Professional Attributes

Strong analytical and problem-solving capabilities.
A keen eye for detail, data quality, and engineering standards.
A practical, delivery-focused approach to solving complex data challenges.
Strong communication skills and the ability to explain technical concepts to both technical and non-technical stakeholders.
Comfortable collaborating with developers, architects, analysts, testers, product teams, and client representatives.
Proven ability to work effectively in an agile, cross-functional environment.
Ability to take ownership of work while contributing positively to the wider team.
Adaptable and comfortable working with evolving requirements, technologies, and delivery priorities.
A commitment to producing secure, maintainable, well-tested, and well-documented solutions.

Inclusion & Diversity at Zinkworks

At Zinkworks, we are deeply committed to fostering a culture of diversity, inclusion, and belonging. We believe that our strength lies in the unique backgrounds, perspectives, and experiences of our team members. By embracing an inclusive environment, we empower innovation and collaboration across all levels of our organisation. Based in Ireland, we are proud to support and engage with our local communities through meaningful initiatives that promote equity and opportunity. Our commitment extends beyond the workplace, as we actively contribute to creating a more inclusive and connected society for all.