Data Operations Engineer

Marathon Petroleum Corporation is hiring a Data Operations Engineer to design, build, automate and integrate solutions across its enterprise data platforms. The role supports reliable, secure and governed platform operations, data integrations, deployments, monitoring and reusable automation.

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

Preferred

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

Cloud Data Platforms

Data Pipelines

Data Integrations

SQL

Python

APIs

Production Support

Incident Triage

Deployment Validation

Documentation

Benefits

Access to health, vision, and dental insurance

Paid time off

401k matching program

Paid parental leave

Educational reimbursement

Eligible for a discretionary company-sponsored annual bonus program

Company

Integrated downstream and midstream energy and refining company

Findlay, Ohio, USA

http://www.marathonpetroleum.com/

Founded in 1887

10001+ employees

Qualifications

Bachelor's Degree in Information Technology, related field or equivalent experience required

Two (2) or more years of relevant experience required

Ability to learn quickly, follow standards, collaborate across teams and deliver reliable solutions

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 scrubbing, also called data cleansing, is the process of amending or removing data in a database that is incorrect, incomplete, improperly formatted, or duplicated

Knowledge of ethical considerations related to data usage, data-driven technologies and strategies to mitigate biases in data-driven decision-making

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 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

Proficiency in implementing and managing data governance frameworks, policies, and standards to ensure data quality, integrity, and compliance

Familiarity with data governance tools and technologies that facilitate data management, metadata management, and data governance workflows

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

Strong understanding of data quality dimensions, methodologies, and best practices to establish and maintain data quality standards and processes

Proficiency in data stewardship practices, including data classification, metadata management, data lineage, and data lifecycle management

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) 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

Ability to design and implement reusable automation, scripts and workflows that reduce manual effort and improve consistency across enterprise data platform operations

Knowledge of cloud-based data platforms such as Azure, Databricks and Cognite Data Fusion, including platform services, data workflows and operational support practices

Ability to support API-based integrations that connect data platforms, source systems, streaming services, observability tools and enterprise applications

Familiarity with automated deployment, release, configuration and validation practices using approved DevOps and CI/CD methods

Ability to monitor platform health, pipeline execution, alerts, telemetry and data refreshes to support reliable data platform operations

Ability to investigate operational issues, document findings, escalate complex incidents and support timely resolution of platform and data workflow problems

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

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

Two (2) or more years of relevant experience required.

Required skills

Azure, Databricks, Cognite Data Fusion, SQL, Python, API Integration, DevOps and CI/CD, Identity and Access Management (IAM), Platform Automation, Platform Monitoring and Observability, Data Governance, Data Quality Management

Experience

2+ years