OCI Strategic Customer Engineering is seeking a highly experienced Principal Data Engineer with deep expertise in Data Engineering, Business Intelligence, and Analytics Platforms. This role is ideal for a technical leader who combines hands-on data engineering capabilities with strong program leadership to deliver scalable, data-driven solutions across Oracle Cloud Infrastructure.
This position focuses on designing and implementing data platforms, analytics solutions, and business intelligence capabilities that enable executive decision-making, operational excellence, customer insights, and strategic planning. The ideal candidate is equally comfortable building data pipelines and reporting frameworks as they are driving complex cross-functional initiatives across engineering, product, operations, and executive organizations.
You are a builder who drives outcomes—not simply a facilitator. You possess strong technical depth, high judgment, a bias for action, and the ability to influence teams across large organizations toward a common vision and measurable business results.
You will partner with teams throughout OCI to develop scalable data solutions, establish trusted business metrics, automate reporting processes, and lead strategic initiatives that impact both product and business outcomes. The work is highly visible, customer-focused, and spans multiple organizations across OCI.
Basic Qualifications
- BS degree or equivalent experience in Computer Science, Engineering, Information Systems, Data Science, or related field
- 7+ years of experience in Data Engineering, Analytics Engineering, Technical Program Management, Software Engineering, or related technical roles
- Strong experience designing, building, and maintaining large-scale data pipelines, ETL/ELT frameworks, and cloud-based data platforms
- Experience developing Business Intelligence solutions, executive dashboards, KPI frameworks, and operational reporting systems
- Advanced SQL skills and experience working with large-scale datasets
- Experience with data modeling, data warehousing, analytics platforms, and reporting architectures
- Strong understanding of cloud technologies, distributed systems, and software development lifecycles
- Demonstrated ability to analyze complex datasets and translate findings into actionable business recommendations
- Experience partnering with engineering, product, operations, and business stakeholders to define requirements and deliver scalable data solutions
- Strong written and verbal communication skills with the ability to communicate effectively across technical and executive audiences
- Proven ability to lead large, cross-functional initiatives and drive execution across organizational boundaries
Preferred Qualifications
- MS degree or equivalent experience in Computer Science, Data Engineering, Analytics, or related field
- 10+ years of experience in Data Engineering, Analytics Platforms, Business Intelligence, Technical Program Management, or Software Development
- Experience building enterprise-scale data lakes, data warehouses, and analytics platforms
- Experience with cloud-native architectures, distributed systems, and OCI services
- Experience with technologies such as Spark, Kafka, Airflow, Databricks, Snowflake, BigQuery, OCI Data Flow, or similar platforms
- Experience with Oracle Analytics Cloud (OAC), Tableau, Power BI, Looker, or comparable BI platforms
- Experience implementing data governance, data quality, metadata management, and observability frameworks
- Experience developing self-service analytics solutions and semantic data models
- Experience working directly with large enterprise customers and strategic cloud initiatives
Responsibilities Data Engineering & Analytics Leadership
- Design, build, and scale data pipelines that aggregate information from multiple OCI systems and services.
- Develop robust data models, datasets, and reporting frameworks that provide actionable insights for engineering, operations, customer success, and executive leadership.
- Architect and implement scalable analytics platforms that support strategic customer programs and operational decision-making.
- Design and maintain enterprise-grade data solutions that improve visibility into customer adoption, operational health, service performance, and business outcomes.
- Build and automate data ingestion, transformation, and reporting processes to reduce manual effort and improve data accuracy.
- Establish data quality, governance, lineage, and observability standards across critical business datasets.
- Partner with engineering teams to define telemetry, instrumentation, and data collection strategies.
- Perform deep analysis of large and complex datasets to identify trends, opportunities, risks, and operational bottlenecks.
- Drive adoption of modern data engineering best practices, tools, and technologies across the organization.
Business Intelligence & Executive Reporting
- Design and deliver Business Intelligence solutions that provide actionable visibility into customer health, operational performance, and strategic business objectives.
- Develop executive dashboards, scorecards, KPI frameworks, and reporting solutions used by senior leadership for decision-making.
- Partner with business leaders to define success metrics, operational indicators, and reporting requirements.
- Build scalable semantic models and reporting datasets that enable self-service analytics across multiple organizations.
- Transform raw operational and engineering data into meaningful business insights and recommendations.
- Standardize reporting methodologies and establish trusted sources of truth for key organizational metrics.
- Support strategic planning, investment decisions, and customer engagement initiatives through data-driven analysis.
Technical Program Management & Strategic Execution
- Lead large, complex, cross-functional initiatives spanning engineering, product, operations, and executive leadership teams.
- Break down ambiguous business problems into actionable technical workstreams and measurable deliverables.
- Develop functional specifications and drive successful execution from concept through delivery.
- Identify process gaps and establish scalable mechanisms that improve organizational efficiency and execution.
- Manage program schedules, dependencies, risks, and stakeholder communications.
- Anticipate bottlenecks, proactively manage escalations, and balance technical constraints with business priorities.
- Drive alignment across OCI organizations toward shared objectives and customer outcomes.
- Lead interactions with cross-functional teams consisting of Engineers, Product Managers, Architects, Customer Success leaders, and Executive Leadership.
- Thrive in a fast-paced, highly ambiguous environment while maintaining focus on delivering measurable business value.
What Success Looks Like
- Trusted data platforms and BI solutions become the foundation for decision-making across OCI Strategic Customer Engineering.
- Executive leaders have real-time visibility into customer outcomes, operational performance, and business health.
- Manual reporting processes are automated and replaced with scalable, self-service analytics capabilities.
- Strategic customer programs execute more effectively through improved data accessibility, insight generation, and operational transparency.
- Cross-functional teams align around a common set of metrics, objectives, and business outcomes.
- Data-driven insights directly influence customer success, operational excellence, and OCI growth initiatives.
Key Responsibilities
Data Processing & Pipelining – Data Requirements, Collection, and Infrastructure:
Mentors less experienced team members to identify data requirements and business objectives of a project or initiative.
Provides expertise on the design and participates in building of data infrastructure to optimize data processing from a variety of data sources.
Independently analyzes, designs, and troubleshoots data flows based on business needs.
Participates and designs architecture, performance, and security reviews of the technical solution.
Adjusts data collection processes that involve indexing and query optimizations for optimal performance.
Builds Extract, Transform, and Load (ETL) pipelines to support efficient and scalable data collection and extraction.
Engages with and holds the upstream and downstream teams accountable for the predefined service level agreements (SLAs).
Manages relationships with the data providers.
Data Processing & Pipelining – Data Governance:
Independently designs and implements data governance policies and procedures for data handling (e.g., data retention) to manage data consistency, integrity, accuracy, and reliability throughout the data lifecycle.
Leads the execution of redaction processes for Personally Identifiable Information (PII) and Protected Health Information (PHI) data, ensuring compliance with data privacy and security standards.
Ensures minimal data collection and usage in accordance with data minimization principles.
Follows data security measures to protect data from unauthorized access, use, disclosure, alteration, or destruction, proactively identifying and escalating potential issues.
Ensures data compliance with relevant laws, regulations, and industry standards.
Data Processing & Pipelining – Data Validation & Quality Assurance:
Provides expertise on and participates in the design and implementation of rigorous data validation and integrity checks, proactively mitigating data quality issues that could impact data pipeline and model performance.
Mentors less experienced team members to define data annotation and labeling processes to ensure data quality.
Identifies opportunities for automation of data validation and governance, and implements them.
Independently corrects deviations and non-conformance when identified.
Data Pipeline and Solutions Engineering – Pipeline Design:
Leverages advanced knowledge of ETL processes to design, develop, and optimize automated, scalable, and efficient data pipeline architectures to build reusable data products.
Implements advanced data storage solutions to store the processed data to be used in a scalable, optimized, and efficient way for access and analysis.
Mentors less experienced team members to manage the flow of data pipeline and storage day-to-day operations.
Data Pipeline and Solutions Engineering – Data Solutions Engineering:
Works independently and collaboratively in an agile development environment with other engineers to develop, maintain, and debug advanced data solutions that are scalable, efficient, cost effective, and reliable.
Reviews runnable code and works on testing and debugging of data solutions with less experienced team members.
Evaluates new technologies to create more robust data solutions.
Enforces and documents code standards and guidance within the team.
Creates documentation for design decisions, and obtains feedback from the broader architecture team before implementing.
Gathers data and evidence to secure necessary approvals.
Core Responsibilities
Planning & Execution:
Manages and coordinates moderately complex tasks, monitoring timelines and deliverables to ensure timely completion and adherence to requirements for a moderately sized project or initiative.
Efficiently delegates, monitors, and prioritizes work across multiple projects, providing technical oversight and adjusting plans to address shifts in resources or timelines.
Collaboration & Partnership:
Collaborates across the organization to align on expectations and achieve shared objectives.
Leverages understanding of business leaders, stakeholders, and/or customers to ensure proposed solutions meet their needs.
Supports inclusivity by actively seeking and listening to diverse perspectives, ensuring others feel heard and respected.
Problem Solving:
Identifies and addresses moderately complex issues by analyzing a wide range of data and/or information to identify solutions in accordance with standard practices.
Proactively escalates unresolved or critical issues with a thorough assessment and suggests potential solutions.
Reviews, contributes to, and documents problem solving strategies.
Continuous Learning:
Pursues learning opportunities to expand knowledge and skills and/or tools in new areas and stays abreast of the latest industry trends and best practices.
Proactively seeks and leverages ongoing feedback and training to improve skills.
Coaches and mentors junior team members, fostering continuous learning and knowledge sharing within and across teams.
Continuous Improvement:
Develops ideas, recommends updates, and/or collaborates on the implementation of process improvements to increase the efficiency and effectiveness of processes, protocols, and workflows across teams, and evaluates the impact on key stakeholders.
Solicits feedback from others on ideas for alternative approaches and methods for continued improvement.
Performance and Development:
Contributes to the talent development pipeline by participating in candidate interviews, assessing candidates, and providing hiring recommendations.
Education and/or Experience:
11 years of experience in software engineering, data engineering, data science, computer science, or information systems
OR
Bachelor's Degree in Software Engineering, Data Science, Computer Science, Information Systems, or related field AND 7 years of experience in software engineering, data engineering, data science, computer science, or information systems
OR
Master's Degree in Software Engineering, Data Engineering, Data Science, Computer Science, Information Systems, or related field AND 5 years of experience in software engineering, data engineering, data science, computer science, or information systems
OR
Doctorate in Computer Science, Data Science, Software Engineering, Information Systems, or related field AND 3 year of experience in software engineering, data engineering, data science, computer science, or information systems.
Job Skills:
Same skills as prior level plus;
Data Modeling Demonstrated proficiency in designing data models to facilitate accurate and efficient data management.
Innovation Demonstrated ability in or knowledge of innovation, including generating or supporting new ideas, technologies, or processes for organizational growth.
Prototyping Demonstrated experience creating and refining prototypes for system validation and stakeholder feedback.
API Integration Demonstrated ability to build and manage robust API integrations for seamless interoperability between systems.
Cloud Computing Demonstrated ability in or knowledge of cloud computing, including deploying, managing, and securing cloud environments and applications.
Preferred Job Qualifications
Education and/or Experience:
12 years of experience in software engineering, data engineering, data science, computer science, or information systems
OR
Bachelor's Degree in Software Engineering, Data Science, Computer Science, Information Systems, or related field AND 8 years of experience in software engineering, data engineering, data science, computer science, or information systems
OR
Master's Degree in Software Engineering, Data Engineering, Data Science, Computer Science, Information Systems, or related field AND 6 years of experience in software engineering, data engineering, data science, computer science, or information systems
OR
Doctorate in Computer Science, Data Science, Software Engineering, Information Systems, or related field AND 4 years of experience in software engineering, data engineering, data science, computer science, or information systems.