Data Engineer (W2 and Locals only)

As a Senior Data Engineer  you will:

• Develop and optimise large-scale data processing solutions using Scala, Spark, SQL, and modern data platform technologies for the components and pipelines you own.
• Build and operate trusted data processing pipelines that handle advertiser, customer, and measurement datasets within secure cloud environments (AWS, Google Cloud Platform, Azure) and approved data-sharing ecosystems.
• Partner with Product, Data Science, Security, Privacy, and Platform Engineering teams to deliver privacy-preserving attribution, measurement, forecasting, and analytics features.
• Build, schedule, and maintain highly scalable batch and streaming data workflows using orchestration frameworks and cloud-native data services.
• Implement data classification, access controls, and privacy-preserving processing techniques to ensure sensitive datasets and identifiers are handled in accordance with security and compliance requirements.
• Contribute to the design and operation of clean-room and trusted data-sharing environments, ensuring only approved aggregated or privacy-protected outputs are made available for downstream consumption.
• Build observability, monitoring, and operational tooling to ensure reliability, performance, and compliance of data processing platforms.
• Troubleshoot complex data platform, performance, and pipeline issues across distributed systems.
• Contribute to technical design, engineering best practices, and operational excellence for the components and pipelines you own.
• Mentor junior engineers, perform thorough code reviews, and contribute to a strong engineering culture within your team.
• Continuously improve Epsilon''s attribution, measurement, forecasting, and privacy-preserving analytics capabilities through the features and pipelines you ship.
• Strong written and verbal English communication skills are required.
• Good understanding of Agile/SCRUM methodologies and experience working within cross-functional product development teams.

Core technical skills

• 5+ years of Data Engineering experience with strong Scala programming and hands-on Apache Spark expertise for large-scale distributed data processing on AWS and/or Google Cloud Platform.
• Strong Python development skills for data pipelines, platform tooling, automation, and infrastructure modules .
• Advanced SQL skills across relational databases, cloud data warehouses, and lakehouse platforms; experience handling TB-scale datasets .
• Experience designing, building, and maintaining batch and streaming data pipelines.
• Strong understanding of data warehousing, dimensional modelling, data quality, partitioning, and performance optimisation.
• Experience with distributed data processing and modern lakehouse architectures (Databricks, Delta Lake, Apache Spark, or equivalent) .
• Experience building and operating distributed data platforms at scale.
• Experience with workflow orchestration platforms such as Airflow, Databricks Workflows, AWS Step Functions, or equivalent DAG-based systems.
• Git or equivalent source control; unit, integration, and automated testing frameworks
• Cloud-native development experience across AWS and/or Google Cloud Platform.
• Strong software engineering practices including CI/CD, code reviews, observability, and production support .
• Ability to take end-to-end ownership of features and pipelines, collaborate effectively across teams, mentor junior engineers, and deliver within tight deadlines.

Trusted environment execution skills (required)

• Hands-on experience operating data pipelines within trusted data environments, clean rooms, secure data-sharing platforms, or equivalent privacy-preserving analytics environments
• Experience working with sensitive datasets containing PII, customer identifiers, advertiser data, or regulated information
• Experience applying fine-grained access controls, data governance policies, and policy-based enforcement for sensitive datasets, including PII, PCI, and other regulated-data classification tiers
• Working knowledge of privacy-preserving data processing techniques including tokenisation, pseudonymisation, aggregation-before-export, and differential privacy concepts
• Experience building or supporting clean-room, measurement, attribution, audience analytics, partner data-sharing, or privacy-preserving reporting solutions
• Understanding of trust boundaries, secure data-sharing patterns, and zero-trust principles, including environments where only aggregated, anonymised, tokenised, or privacy-protected outputs may leave the trusted perimeter
• Experience with encryption, key management, and secure handling of sensitive data using cloud-native security services
• Experience helping build observability, monitoring, and alerting controls to detect anomalous data movement, policy violations, and potential data leakage events
• Strong understanding of cloud-native security and governance