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Job Title: Manager Data Engineering - Retail Media Network Platform
Location- Houston, TX/ Onsite 3 days a week/ If not Houston can be located near a PS office in Chicago, Atlanta, Miami, New York, Boston as they are still required to be onsite 3 days a week.
Duration- 6 months
Responsibilities:
The Data Manager will be responsible for overseeing end-to-end data management across the Retail Media Network platform, including data ingestion, transformation, integration, quality assurance, governance, and analytics enablement. This role will manage the delivery of reliable, scalable, and business-ready datasets that support media reporting, campaign performance measurement, retail operations insights, and executive dashboards.
The Data Manager will work closely with data engineers, data analysts, IT teams, marketing teams, and business stakeholders to ensure data pipelines, data models, and reporting assets are accurate, well-governed, and aligned with business objectives.
Key Responsibilities
Data Strategy and Management
• Own the overall data management approach across source systems, cloud data platforms, analytics layers, and reporting tools.
• Define standards for data ingestion, transformation, validation, documentation, and usage.
• Ensure data assets are structured to support retail media use cases, including campaign reporting, audience insights, media performance, and promotional analytics.
• Partner with business stakeholders to understand data requirements and translate them into scalable data solutions.
Data Pipeline and Integration Oversight
• Oversee the design, development, and maintenance of data pipelines that extract data from systems such as Snowflake, Microsoft Azure, Google BigQuery, and other enterprise platforms.
• Ensure efficient data ingestion, transformation, and loading processes across source and target systems.
• Manage integration between multiple data sources to ensure consistency, reliability, and usability of data.
• Collaborate with IT and engineering teams to troubleshoot pipeline issues, optimize data flows, and improve system performance.
• Establish error handling, monitoring, and exception management processes to maintain data integrity.
• Support cloud-based data integration patterns using Microsoft Azure services where applicable.
Data Quality, Governance, and Compliance
• Define and enforce data quality standards, including validation, normalization, standardization, and reconciliation processes.
• Implement governance best practices to ensure data accuracy, consistency, security, and compliance.
• Monitor data quality issues and coordinate resolution across engineering, analytics, and business teams.
• Maintain clear documentation for data sources, data definitions, transformation logic, data mappings, and business rules.
• Ensure data handling practices align with internal governance, privacy, and compliance policies.
Data Modeling and Analytics Enablement
• Oversee data modeling efforts in platforms such as Google BigQuery, Snowflake, Microsoft Azure, and Looker.
• Guide the development of LookML models to support scalable and reusable reporting.
• Ensure datasets are structured properly for dashboarding, self-service analytics, and advanced analysis.
• Partner with analysts to validate business logic, metrics definitions, and reporting outputs.
• Support the creation of actionable reports and dashboards for marketing, media, retail, and leadership teams.
Retail Media and Business Insights Support
• Manage data related to retail media campaigns, media datasets, convenience store operations, marketing performance, and promotional activity.
• Work with marketing and media teams to ensure data is available for campaign optimization, measurement, and reporting.
• Support analysis of media performance, retail behavior, customer engagement, and operational trends.
• Help translate data insights into business recommendations for stakeholders.
Team Leadership and Cross-Functional Collaboration
• Lead and coordinate data engineers, data analysts, and other technical resources involved in data delivery.
• Facilitate communication between business teams, IT, analytics, and engineering groups.
• Participate in planning sessions, project reviews, and stakeholder meetings to align data initiatives with business goals.
• Prioritize data workstreams, manage dependencies, and ensure timely delivery of data products.
• Promote best practices in data engineering, analytics, documentation, and governance.
Required Skills and Qualifications
• Proven experience in data management, data engineering, analytics, or business intelligence.
• Strong understanding of data pipelines, ETL/ELT processes, data integration, and data transformation.
• Experience with cloud data platforms such as Google BigQuery, Snowflake, and Microsoft Azure.
• Familiarity with Microsoft Azure data services and cloud-based data integration patterns.
• Strong SQL skills for querying, validation, and data analysis.
• Experience with dashboarding and BI tools, especially Looker.
• Working knowledge of LookML and semantic data modeling.
• Understanding of Python-based data processing and libraries such as Pandas, NumPy, or PySpark.
• Strong knowledge of data quality, data governance, metadata management, and compliance principles.
• Experience working with media datasets, marketing data, or retail/convenience store data.
• Excellent problem-solving, analytical, communication, and stakeholder management skills.
• Ability to manage large datasets and ensure accuracy, scalability, and reliability.
Preferred Qualifications
• Experience in Retail Media Networks, digital media, ad tech, or marketing analytics.
• Experience managing cross-functional data teams or data delivery workstreams.
• Knowledge of campaign performance measurement, audience data, media reporting, and retail transaction data.
• Familiarity with modern cloud-based data architectures, especially Microsoft Azure-based data ecosystems.
• Experience defining KPI frameworks, metric governance, and reporting standards.
• Ability to bridge technical and business teams and explain complex data concepts clearly.
Success Measures
• Reliable and scalable data pipelines across source systems and analytics platforms.
• High data quality and reduced reporting discrepancies.
• Clear ownership of data definitions, transformation logic, and governance standards.
• Faster delivery of trusted dashboards and analytics outputs.
• Strong alignment between business requirements and technical data solutions.
• Improved ability for marketing, media, and retail teams to make data-driven decisions