Senior Data Operations Engineer

Marathon Petroleum Corporation•Houston, TX

23h•Onsite

About The Position

An exciting career awaits you At MPC, we’re committed to being a great place to work – one that welcomes new ideas, encourages diverse perspectives, develops our people, and fosters a collaborative team environment. Position Summary Data, Analytics and AI is advancing Marathon Petroleum’s data ecosystem through trusted, governed and reusable data products that enable faster decisions, operational excellence and AI-driven innovation. The Senior Data Operations Engineer is a senior technical leader responsible for the reliability, scalability, security and performance of MPC’s enterprise data platforms. This role leads platform modernization, automation and operational excellence across Data Engineering, Platform Engineering, SRE, FinOps, Governance and Data Product Operations, with emphasis on cloud-native platforms including Databricks, Azure, Cognite, streaming technologies, observability solutions and enterprise integrations. The successful candidate will establish standards, improve reliability, optimize platform performance, mentor engineers and partner across Architecture, Engineering, Security, Governance and Business teams to deliver scalable, secure and high-performing data platform capabilities. This position belongs to a family of jobs with increasing responsibility, competency, and skill level. Actual position title and pay grade will be based on the selected candidate’s experience and qualifications.

Requirements

Bachelor’s Degree in Information Technology, related field or equivalent experience required.
Five (5) or more years of relevant experience required.
Senior-level experience in data engineering, platform engineering, cloud engineering, or platform operations preferred.
Experience supporting enterprise-scale cloud data platforms.
Experience operating mission-critical analytics and data platforms.
Demonstrated success leading platform modernization and automation initiatives.
Experience supporting production environments with high availability requirements.
Data Classification - Knowledge of the process of formally grouping Configuration Items by type, e.g. software, hardware, documentation, environment, application. Knowledge of the process of formally identifying Changes by type, e.g. project scope change request, corrective change request, innovative function change request, technical infrastructure change request. And, knowledge of the process of formally identifying Incidents, Problems and Known Errors by origin, symptoms and cause.
Data Cleansing - Data scrubbing, also called data cleansing, is the process of amending or removing data in a database that is incorrect, incomplete, improperly formatted, or duplicated.
Data Ethics - Knowledge of ethical considerations related to data usage, data-driven technologies and strategies to mitigate biases in data-driven decision-making.
Data Governance - Ability to establish and oversee a set of procedures, policies, and standards that ensure the effective and efficient management of an organization's data assets. This includes ensuring data quality, compliance with relevant laws and regulations, and secure data handling practices. It also involves the coordination between different departments to ensure that data is accurate, accessible, and used responsibly and ethically.
Data Governance Communication - Data governance communication involves the effective dissemination of policies, guidelines, and best practices related to the management and use of data within an organization. It aims to ensure clear understanding, compliance, and collaboration among stakeholders, fostering a culture of responsible and consistent data practices.
Data Governance Framework - Proficiency in implementing and managing data governance frameworks, policies, and standards to ensure data quality, integrity, and compliance.
Data Governance Tools - Familiarity with data governance tools and technologies that facilitate data management, metadata management, and data governance workflows.
Data Privacy - Ability to understand and implement practices that ensure the protection and confidential handling of personal and sensitive information. This includes knowledge of relevant laws and regulations (such as GDPR or HIPAA), the ability to design and enforce policies that safeguard data, and the skills to manage data access rights and consent protocols.
Data Quality Management - Strong understanding of data quality dimensions, methodologies, and best practices to establish and maintain data quality standards and processes.
Data Stewardship - Proficiency in data stewardship practices, including data classification, metadata management, data lineage, and data lifecycle management.
Identity Access Management (IAM) - Identity and access management (IAM) is a framework of business processes, policies and technologies that facilitates the management of electronic or digital identities, ensuring that the right users have the appropriate access to technology resources.
Master Data Management (MDM) - Master Data Management (MDM) is a method of managing and organizing an organization's critical data to ensure consistency, accuracy, and coherence across various systems. It involves establishing a single, authoritative source for key data entities, such as customer information, product details, and other essential business data.
MINIMUM QUALIFICATIONS: Bachelor’s Degree in Information Technology, related field or equivalent experience.
5+ years of relevant experience

Nice To Haves

As an energy industry leader, our career opportunities fuel personal and professional growth.

Responsibilities

Leads platform scalability and performance tuning initiatives.
Mentors less experienced team members in best operational practices.
Collaborates with architects to design resilient and robust platforms.
Drives the resolution of critical platform issues.
Engages with vendors and partners for platform solutions and enhancements.
Implements robust monitoring and alerting mechanisms.
Participates in strategic planning for platform growth and evolution.
Drives the adoption of new technologies and tools for platform operations.
Ensures compliance with industry regulations related to data storage and operations.
Engages with stakeholders to understand and cater to their platform needs.
Standardize reusable infrastructure patterns with IaC, CI/CD, and DevOps practices.
Create self-service provisioning and deployment workflows to improve consistency and reduce manual effort.
Establish reusable engineering standards, templates, and operating frameworks.
Strengthen observability through telemetry standards, runbooks, incident response, and root cause analysis.
Define readiness criteria and improvement practices for production platform services.
Operate enterprise platforms across Databricks, Azure Lakehouse, streaming, integration, observability, and Cognite.
Translate architecture standards into operating patterns for scalable analytics and AI workloads.
Embed security, resiliency, compliance, and governance controls into platform operations.
Analyze capacity, utilization, workload, and cost drivers with Cybersecurity and FinOps partners.
Define and manage KPIs, SLAs, SLOs, and platform health metrics.
Provide senior technical direction for operations, standards adoption, and engineering execution.
Facilitate reviews and technical decisions for complex platform scenarios.
Advance maturity by codifying lessons learned and promoting consistent practices across Data Engineering and Platforms teams.