Lead Data Engineer

Hybrid At least 2 days per week in office in Chicago, IL Our client seeks a Lead Data Engineer to design, build, and optimize large-scale data processing for attribution, measurement, forecasting, and privacy-preserving analytics. The role requires leadership of a major workstream, strong Scala, Spark, SQL, and Python skills, and cloud data platform expertise. The engineer will partner across Product, Data Science, Security, Privacy, and Platform Engineering to deliver secure, scalable, and compliant data solutions. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance. Rate: $75.00 to $85.00/hr. w2 Responsibilities Lead design, implementation, and optimization of large-scale data processing using Scala, Spark, SQL, and modern data platforms for attribution. Design and operate trusted data pipelines handling advertiser, customer, and measurement datasets across AWS, GCP, Azure, and approved ecosystems. Collaborate with Product, Data Science, Security, Privacy, and Platform Engineering to deliver privacy-preserving attribution, measurement, forecasting, and analytics. Own scalable batch and streaming workflows using orchestration frameworks and cloud-native services. Implement data classification, access controls, and privacy-preserving techniques aligned with security and compliance requirements. Drive clean-room and trusted data-sharing environments, exposing only aggregated or privacy-protected outputs. Build observability, monitoring, alerting, and operational tooling for reliability, performance, and compliance. Troubleshoot complex platform, performance, and pipeline issues across distributed systems. Influence technical design, architecture, and best practices in partnership with senior engineering leadership. Mentor engineers, lead design and code reviews, and provide technical leadership. Ensure all sensitive processing occurs within controlled, auditable boundaries with no unintended egress of PII or proprietary signals. Define data handling standards and document trust boundaries, data contracts, lineage, and permitted movement between zones. Apply privacy-preserving computation, including aggregation-before-export, pseudonymization, tokenization, differential privacy concepts, and privacy-aware reporting. Implement encryption, key management, and secure handling using cloud-native security and governance services. Support audits, compliance, governance reviews, and secure data-sharing initiatives. Experience Requirements 8+ years in Data Engineering with strong Scala and extensive Apache Spark on AWS and/or GCP. Strong Python for pipelines, tooling, automation, and infrastructure modules. Advanced SQL across relational, cloud warehouses, and lakehouse platforms handling TB-scale datasets. Design, build, and maintenance of batch and streaming pipelines. Data warehousing, dimensional modeling, data quality, partitioning, and performance optimization. Distributed data processing and lakehouse architectures such as Databricks, Delta Lake, or Apache Spark. Operating distributed data platforms at scale with orchestration tools like Airflow, Databricks Workflows, or AWS Step Functions. Git-based workflows and automated testing frameworks. Cloud-native development on AWS and/or GCP with CI/CD, code reviews, observability, and production support. Proven technical leadership, mentorship, and delivery within tight timelines. Trusted environment execution: clean rooms or secure data-sharing platforms, handling PII and regulated data, fine-grained access controls, governance policies, and policy enforcement. Familiarity with tokenization, pseudonymization, aggregation-before-export, and differential privacy concepts. Experience with measurement, attribution, audience analytics, or privacy-preserving reporting solutions. Data lineage and governance tooling such as Unity Catalog, AWS Glue Data Catalog, Apache Atlas, or OpenLineage. Understanding of trust boundaries, secure data-sharing patterns, and zero-trust data architecture. Experience documenting data contracts, flows, lineage, and permitted data movement between zones and domains. Encryption, key management, and secure handling of sensitive data with cloud-native security services. Design of observability and alerting to detect anomalous movement, policy violations, and potential leakage. Experience in environments where only aggregated, anonymized, tokenized, or privacy-protected outputs may leave trusted boundaries. Strong written and verbal English communication and experience in Agile/SCRUM. Good to have: Databricks, AWS Clean Rooms or PETs, advertising and retail media platforms, collaboration with Security/Privacy/Risk/Compliance, ELK/Grafana/OpenTelemetry, Docker/Kubernetes, and secure design reviews. #J-18808-Ljbffr Eliassen Group