Data Engineer, Data Cloud International
<p><strong>The Role</strong></p>
<p>Zeta Global is seeking a Data Engineer to help accelerate the operationalization of Zeta’s Data Cloud across international markets.</p>
<p>Sitting within the Data Cloud International / commercial data partnerships function, this position will work closely with Business Operations, Applications, Product, Engineering, Compliance, and external partner technical teams.</p>
<p>The focus is practical data engineering: turning high-potential data partnerships into usable, repeatable, revenue-generating Data Cloud assets that support product innovation, market expansion, and commercial growth through disciplined evaluation, ingestion, transformation, validation, and documentation.</p>
<p>You will work primarily across AWS-based data workflows, using tools such as S3, Athena, Glue, SQL, Python, APIs, and orchestration frameworks such as Airflow. This is a hands-on, delivery-focused position suited to someone who enjoys turning technical data requirements into practical workflows.</p>
<p>You will also use AI-assisted tools and automation techniques to improve data evaluation, documentation, workflow generation, and internal productivity.</p>
<p>The position is based in Copenhagen, Denmark. Candidates should be able to commute regularly to the Copenhagen office, located in Copenhagen or near Copenhagen Central Station.</p>
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<p><strong>Roles & Responsibilities</strong></p>
<p>• Build and automate data workflows for new and existing data partnerships.<br>• Create repeatable ingestion, transformation, and replication processes for partner data feeds.<br>• Evaluate incoming datasets for structure, usability, coverage, completeness, and quality.<br>• Write scripts to transform, normalize, move, and prepare data for analysis or downstream use.<br>• Use SQL and Athena to query large datasets, validate outputs, and generate reporting tables or extracts.<br>• Build lightweight validation checks for files, schemas, counts, formats, and expected values.<br>• Produce clear technical documentation, including field mappings, data dictionaries, process notes, data flow summaries, and partner integration documentation.<br>• Use AI-assisted tools where appropriate to accelerate data investigation, documentation, code generation, workflow prototyping, and repeatable analysis.<br>• Communicate with technical contacts at data partners via email and occasional technical calls.<br>• Help translate partner data delivery requirements into practical ingestion and automation workflows.<br>• Work with internal teams across Operations, Product, Engineering, Compliance, and Data Cloud.<br>• Contribute to reusable templates, naming conventions, and documentation standards for partner data onboarding.</p>
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<p><strong>Required Qualifications</strong></p>
<p>• Strong written and spoken English is required, as the role works across international teams and external data partners.<br>• 3–5 years of relevant experience, or equivalent practical experience, in data engineering, analytics engineering, technical data operations, cloud data automation, or a similar role.<br>• Practical experience writing SQL to query, validate, and transform data.<br>• Hands-on experience with Python for scripting, automation, or data manipulation.<br>• Familiarity with AWS data services, especially S3 and Athena; experience with Glue is strongly preferred.<br>• Understanding of ETL/ELT concepts and how data moves between systems.<br>• Experience working with structured and semi-structured data formats, including large delimited files, JSON, Parquet, or ORC.<br>• Comfortable reading technical documentation and working with API-based data sources.<br>• Ability to work with large datasets and investigate issues in schemas, counts, formats, or transformation outputs.<br>• Comfortable collaborating with internal technical teams and external partner technical contacts.<br>• Able to work independently on defined tasks while escalating ambiguity, blockers, or risks appropriately.</p>
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<p><strong>Required Skills</strong></p>
<p>• Business-level English communication<br>• SQL<br>• Python<br>• AWS S3, Athena, and Glue / Glue Data Catalog<br>• ETL/ELT workflows<br>• Data ingestion and replication<br>• APIs and file-based data exchange<br>• Airflow, Prefect, or similar orchestration tools<br>• Snowflake, Databricks, Redshift, Hive, Presto, or similar data platforms<br>• Git or similar version control<br>• Data validation and quality checks<br>• Data dictionaries, field mappings, and technical documentation<br>• Structured and semi-structured data formats, including JSON, Parquet, ORC, and large delimited files<br>• Working knowledge of S3 policies, IAM permissions, and secure data access patterns<br>• Practical use of AI-assisted development, documentation, or data analysis tools</p>
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