Data Engineer, Data Cloud International

The Role

• Zeta Global is seeking a Data Engineer to help accelerate the operationalization of Zeta’s Data Cloud across international markets.
• 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.
• 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.
• 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.
• You will also use AI-assisted tools and automation techniques to improve data evaluation, documentation, workflow generation, and internal productivity.
• 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.

Roles & Responsibilities

• • Build and automate data workflows for new and existing data partnerships.
• • Create repeatable ingestion, transformation, and replication processes for partner data feeds.
• • Evaluate incoming datasets for structure, usability, coverage, completeness, and quality.
• • Write scripts to transform, normalize, move, and prepare data for analysis or downstream use.
• • Use SQL and Athena to query large datasets, validate outputs, and generate reporting tables or extracts.
• • Build lightweight validation checks for files, schemas, counts, formats, and expected values.
• • Produce clear technical documentation, including field mappings, data dictionaries, process notes, data flow summaries, and partner integration documentation.
• • Use AI-assisted tools where appropriate to accelerate data investigation, documentation, code generation, workflow prototyping, and repeatable analysis.
• • Communicate with technical contacts at data partners via email and occasional technical calls.
• • Help translate partner data delivery requirements into practical ingestion and automation workflows.
• • Work with internal teams across Operations, Product, Engineering, Compliance, and Data Cloud.
• • Contribute to reusable templates, naming conventions, and documentation standards for partner data onboarding.

Required Qualifications

• • Strong written and spoken English is required, as the role works across international teams and external data partners.
• • 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.
• • Practical experience writing SQL to query, validate, and transform data.
• • Hands-on experience with Python for scripting, automation, or data manipulation.
• • Familiarity with AWS data services, especially S3 and Athena; experience with Glue is strongly preferred.
• • Understanding of ETL/ELT concepts and how data moves between systems.
• • Experience working with structured and semi-structured data formats, including large delimited files, JSON, Parquet, or ORC.
• • Comfortable reading technical documentation and working with API-based data sources.
• • Ability to work with large datasets and investigate issues in schemas, counts, formats, or transformation outputs.
• • Comfortable collaborating with internal technical teams and external partner technical contacts.
• • Able to work independently on defined tasks while escalating ambiguity, blockers, or risks appropriately.

Required Skills

• Business-level English communication • SQL • Python • AWS S3, Athena, and Glue / Glue Data Catalog • ETL/ELT workflows • Data ingestion and replication • APIs and file-based data exchange • Airflow, Prefect, or similar orchestration tools • Snowflake, Databricks, Redshift, Hive, Presto, or similar data platforms • Git or similar version control • Data validation and quality checks • Data dictionaries, field mappings, and technical documentation • Structured and semi-structured data formats, including JSON, Parquet, ORC, and large delimited files • Working knowledge of S3 policies, IAM permissions, and secure data access patterns • Practical use of AI-assisted development, documentation, or data analysis tools