Senior Data Engineer

Procter & Gamble is building highly scalable data and analytics capabilities, and you will help lead the technical foundation. This onsite role in Cincinnati, OH offers a senior-level opportunity to own end-to-end data solutions, strengthen engineering standards, and apply responsible AI in production data systems. Total rewards include salary plus bonus (if applicable) and benefits.

What you’ll do

• Lead architectural design and implementation of large-scale data solutions, focused on scalability, performance, security, and cost-efficiency.
• Translate strategic business objectives into architectural blueprints and technical roadmaps by partnering with senior business stakeholders and product owners.
• Influence data strategy and technology choices across multiple teams or domains, driving alignment on standards and architecture.
• Architect, build, and optimize highly resilient ETL/ELT pipelines on modern cloud data platforms, including real-time processing and handling petabyte-scale volumes.
• Design advanced integration patterns across enterprise systems, third-party services, streaming sources, and APIs to ensure high availability and reliability.
• Drive adoption of advanced data processing approaches such as stream processing, graph databases, and data mesh principles.
• Mentor data engineers as a primary technical resource, guiding architectural decisions, complex troubleshooting, and career development.
• Lead code reviews, design discussions, and technical workshops to support continuous improvement and engineering quality.
• Champion and evolve enterprise engineering standards for data, including data quality frameworks, testing automation, CI/CD pipelines, security protocols, documentation standards, and data observability.
• Own critical data solutions from inception through implementation, deployment, monitoring, performance tuning, and incident response for production systems.
• Implement data quality frameworks, observability solutions, and anomaly detection to protect integrity and reliability.
• Evaluate, prototype, and integrate cutting-edge technologies, including Generative AI models and agentic systems, to improve developer productivity and enable novel data solutions.
• Lead thought leadership on the responsible and ethical application of AI in data engineering, with security, privacy, and bias mitigation best practices.
• Support continuous learning and knowledge sharing across the broader engineering organization.
• Use modern development tools and practices, including advanced IDE features, sophisticated Git strategies (monorepos, gitflow), infrastructure as code (IaC), and advanced CI/CD pipelines tailored for data platforms.

What you’ll bring

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a closely related quantitative field.
• 5+ years of progressive data engineering experience with a strong record delivering large-scale, complex data platforms and pipelines.
• Proven experience leading technical projects, mentoring engineers, and influencing architecture across multiple teams.
• Expert proficiency in Python and SQL for complex data manipulation, optimization, performance tuning, and advanced analytics.
• Expert hands-on experience with at least one major modern cloud platform ( Azure preferred, and/or GCP ), including deep knowledge of relevant data services (Azure Synapse, Databricks, Data Factory, Event Hubs, Data Lake Storage; or GCP BigQuery, Dataflow, Pub/Sub, Cloud Storage).
• Extensive experience with distributed data processing technologies (Spark, PySpark, Dask), including performance optimization, cluster management, and resource allocation for petabyte-scale workloads.
• Expert knowledge of advanced data modeling and warehousing approaches (dimensional, Kimball, Inmon, data vault, data mesh) and ability to design optimized, flexible schemas.
• Ability to architect complex data integrations across systems using advanced API integration, message queues (Kafka, Azure Event Hubs), and enterprise-grade data transfer protocols.
• Extensive experience with modern development practices, CI/CD, infrastructure as code (Terraform, ARM templates), and production deployment, monitoring, and management of data and machine learning pipelines.
• Practical leadership in leveraging Generative AI tools and agentic systems to accelerate development and solve complex data problems, including responsible AI principles around data privacy, security, and ethical considerations.
• Exceptional communication and interpersonal skills, including the ability to explain complex technical concepts to senior stakeholders and influence strategic decisions.
• Ability to drive initiatives from conception to completion with architectural and operational responsibility for critical data assets.
• Strong curiosity and passion for continuous learning, staying current on industry trends and emerging data technologies.

Preferred experience

• Deep expertise and certifications in Azure data services (Azure Databricks, Azure Synapse Analytics, Azure Data Factory, Azure Stream Analytics).
• Experience implementing data governance, master data management (MDM), and data lineage solutions.
• Hands-on experience with real-time streaming frameworks (Kafka, Spark Streaming, Flink).
• Strong software engineering fundamentals applied to data platforms (design patterns, clean code principles, microservices architecture).
• Experience with NoSQL databases (Cosmos DB, MongoDB) or graph databases (Neo4j) for specialized use cases.
• Professional or expert-level certifications such as Azure Data Engineer Expert, Databricks Certified Data Engineer Professional, or Google Cloud Professional Data Engineer.
• Experience supporting MLOps initiatives and integrating data pipelines with ML models.

Compensation: $132,200 - $165,300 per year (starting pay range).
Location: Cincinnati General Offices (onsite).
Schedule: Full time.
Job number: R000155516.
Job segmentation: Experienced Professionals.