Responsibilities
- Assist partners in integrating into secure clean room platforms such as Snowflake, LiveRamp, or Databricks.
- Adhere to secure, scalable, and repeatable architectural patterns for clean room deployments across engagements.
- Set up and maintain clean room configurations, including access controls, environment initialization, and release verification.
- Act as the primary technical lead for partner onboarding, coordinating with product, engineering, operations, privacy, and external teams.
- Enforce privacy safeguards including aggregation limits, anonymization methods, permitted query structures, and output validation.
- Deploy and maintain Python-based tools, templates, and reusable assets within data platforms and clean rooms.
- Manage environment provisioning, configuration, package distribution, and version-controlled release cycles.
- Collaborate with software engineering teams to operationalize reusable components for audience, reporting, and partner workflows.
- Ensure consistent deployment of platform elements across partner environments in alignment with engineering standards.
- Develop and enforce fine-grained, role-based access policies across data environments.
- Configure minimal-privilege service accounts, roles, permissions, schemas, shares, and data access models.
- Work with security, privacy, and platform teams to align access controls with internal policies and partner needs.
- Verify that outputs shared with partners meet privacy, security, and business criteria prior to release.
- Build and maintain scalable ELT pipelines using advanced SQL, Snowpark, PySpark, dbt, or comparable technologies.
- Create and manage high-quality Gold-layer datasets for use in audience, measurement, activation, and reporting systems.
- Develop standardized pipeline designs supporting both batch and near real-time processing on platforms like Snowflake or Databricks.
- Convert business and analytical needs into robust, documented, production-grade data solutions.
- Own end-to-end performance, reliability, accuracy, and operational support for assigned data pipelines and products.
- Implement and refine identity resolution logic to align internal data with third-party identifiers such as LUIDs, RampIDs, or TransUnion IDs.
- Enable privacy-compliant identity workflows for audience matching, measurement, activation, and partner integrations.
- Develop validation mechanisms to ensure identity mappings are accurate, secure, and follow approved usage rules.
- Collaborate with internal and external teams to resolve issues related to match rates, data quality, and onboarding inconsistencies.
- Implement automated data quality testing using frameworks like Great Expectations, dbt tests, or custom SQL assertions.
- Define and track data quality metrics including schema changes, spikes in nulls, volume fluctuations, duplicates, referential integrity, and distribution shifts.
- Design comprehensive test plans for partner releases, covering input validation, output checks, regression testing, and privacy compliance.
