Data Operations Engineer

Marathon Petroleum Corporation•San Antonio, TX

1d•Onsite

About The Position

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 Data Operations Engineer is an individual contributor who designs, builds, automates and integrates solutions across MPC’s enterprise data platforms to improve reliability, scalability and engineering productivity. This role applies software engineering, automation, API integration, data engineering and cloud platform practices to connect data platforms, streaming services, observability tools and enterprise systems. The successful candidate will work with senior engineers, architects and platform leads to develop reusable automation, implement repeatable integration patterns, support deployments, troubleshoot issues, strengthen monitoring, and contribute to secure, governed and resilient 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.
Two (2) or more years of relevant experience required.
Experience with cloud data platforms, pipelines, integrations or automation preferred.
Experience with Azure, Databricks, Cognite Data Fusion, SQL, Python, APIs, DevOps or monitoring tools preferred.
Exposure to production support, incident triage, deployment validation and documentation preferred.
Ability to learn quickly, follow standards, collaborate across teams and deliver reliable solutions.
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.
Platform Automation - Ability to design and implement reusable automation, scripts and workflows that reduce manual effort and improve consistency across enterprise data platform operations.
Cloud Data Platforms - Knowledge of cloud-based data platforms such as Azure, Databricks and Cognite Data Fusion, including platform services, data workflows and operational support practices.
API Integration - Ability to support API-based integrations that connect data platforms, source systems, streaming services, observability tools and enterprise applications.
DevOps and CI/CD - Familiarity with automated deployment, release, configuration and validation practices using approved DevOps and CI/CD methods.
Platform Monitoring and Observability - Ability to monitor platform health, pipeline execution, alerts, telemetry and data refreshes to support reliable data platform operations.
Incident Triage and Troubleshooting - Ability to investigate operational issues, document findings, escalate complex incidents and support timely resolution of platform and data workflow problems.
Data Product Enablement - Understanding of how governed data products, contextualized data and reusable platform capabilities support analytics, AI and business decision-making.
Bachelor’s Degree in Information Technology, related field or equivalent experience.
2+ years of relevant experience

Nice To Haves

Experience with cloud data platforms, pipelines, integrations or automation preferred.
Experience with Azure, Databricks, Cognite Data Fusion, SQL, Python, APIs, DevOps or monitoring tools preferred.
Exposure to production support, incident triage, deployment validation and documentation preferred.

Responsibilities

Proactively identifies operational inefficiencies and recommends solutions.
Independently manages platform updates and migrations.
Implements and upholds security protocols for data platforms.
Assists in optimizing the platform for performance and scalability.
Collaborates with other IT departments for seamless system integrations.
Participates in capacity planning and resource allocation.
Maintains system backups and disaster recovery procedures.
Contributes to the automation of routine platform tasks.
Oversees data storage solutions and ensures data integrity.
Stays updated with the latest platform technologies and best practices.
Support reliable operations across enterprise data platforms using established runbooks, standards and procedures.
Build reusable automation, workflows and platform utilities for Cognite Data Fusion, Databricks, Azure and related services.
Support ingestion, transformation, API integration and contextualization for governed data products and operational models.
Monitor platform health, pipeline execution, alerts and data refreshes; investigate issues and escalate with clear analysis.
Assist with automated deployment, release, configuration and validation using approved DevOps and CI/CD practices.
Maintain runbooks, support documentation and lessons learned to improve troubleshooting and reduce recurring issues.
Apply enterprise standards for security, IAM, governance, compliance and data handling.
Collaborate with architects, engineers, platform leads, product teams and vendors to deliver scalable automation and integration solutions.
Execute provisioning, configuration, integration, monitoring and support tasks across data platform services.
Support Cognite Data Fusion, Databricks, Azure Lakehouse, streaming, API integration and observability workflows.
Contextualize assets, time series, events, documents and metadata for analytics, AI and data product activation.
Validate data loads, refreshes, model updates and integration flows across environments.
Follow architecture, platform, security and governance standards for scalable data product capabilities.