Principal Data Engineer

Responsibilities

• Design, build, and scale data pipelines that aggregate information from multiple OCI systems and services.
• Develop robust data models, datasets, and reporting frameworks for engineering, operations, customer success, and executive leadership.
• Architect and implement scalable analytics platforms to 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.
• 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 datasets.
• Partner with engineering teams to define telemetry, instrumentation, and data collection strategies.
• Analyze large and complex datasets to identify trends, opportunities, risks, and bottlenecks.
• Drive adoption of modern data engineering practices, tools, and technologies across the organization.
• Lead cross-functional initiatives spanning engineering, product, operations, and executive leadership.
• Break down ambiguous business problems into actionable technical workstreams and deliverables.
• Develop functional specifications and oversee execution from concept through delivery.
• Identify process gaps and establish scalable mechanisms to improve organizational efficiency.
• Manage program schedules, dependencies, risks, and stakeholder communications.
• Anticipate bottlenecks, manage escalations, and balance technical constraints with business priorities.
• Drive alignment across OCI organizations toward shared objectives and customer outcomes.
• Lead interactions with engineers, product managers, architects, customer success leaders, and executive leadership.
• Operate effectively in a fast-paced, ambiguous environment while delivering measurable business value.

Business Intelligence & Executive Reporting

• Design and deliver BI solutions that provide visible insights into customer health, operations, and strategic objectives.
• Develop executive dashboards, KPI frameworks, and reporting used by senior leadership for decision-making.
• Collaborate with business leaders to define success metrics and reporting requirements.
• Build scalable semantic models and datasets enabling self-service analytics across organizations.
• Transform raw data into meaningful business insights and recommendations; standardize reporting methodologies.

Technical Program Management & Strategic Execution

• Lead large, cross-functional initiatives across engineering, product, operations, and leadership.
• Decompose complex problems into actionable workstreams and measurable deliverables.
• Develop functional specifications and drive end-to-end delivery from concept to production.
• Identify gaps and implement scalable processes that boost efficiency and execution.
• Manage schedules, dependencies, risks, and stakeholder communications; balance constraints with priorities.
• Foster alignment across OCI organizations toward common objectives and customer outcomes.

Data Processing, Governance, and Quality

• Mentor team members to identify data requirements and business objectives; design data infrastructure for diverse sources.
• Design architecture, performance, and security reviews for technical solutions; optimize data collection and indexing.
• Build ETL/ELT pipelines supporting scalable data intake and extraction; manage SLAs with data providers.
• Independently design data governance policies, including retention, privacy redaction, and data minimization.
• Implement data validation and quality checks; automate governance where possible; correct deviations as needed.

Data Pipeline & Solutions Engineering

• Leverage advanced ETL knowledge to design, develop, and optimize scalable data pipelines and reusable data products.
• Implement storage solutions for accessible, analyzable data; review code and mentor junior team members.

Core Responsibilities

• Plan and execute moderately complex tasks; delegate and monitor work across multiple projects.
• Collaborate across the organization to align expectations and achieve shared objectives.
• Identify and solve moderately complex problems using data-driven analysis; escalate when needed.
• Pursue continuous learning, solicit feedback, and mentor junior teammates.
• Propose process improvements and measure impact on stakeholders.
• Support talent development through involvement in candidate interviews and hiring recommendations.

Technologies

• Spark
• Kafka
• Airflow
• Databricks
• Snowflake
• BigQuery
• OCI Data Flow
• Oracle Analytics Cloud (OAC)
• Tableau
• Power BI
• Looker
• SQL
• Oracle Cloud Infrastructure (OCI)

What Success Looks Like

• Trusted data platforms and BI solutions underpin decision-making across OCI Strategic Customer Engineering.
• Executives have real-time visibility into customer outcomes, operational performance, and business health.
• Manual reporting is automated, replaced by scalable, self-service analytics capabilities.
• Strategic customer programs execute more effectively with improved data accessibility and transparency.
• Cross-functional teams align around a common set of metrics and outcomes.
• Data-driven insights influence customer success, operational excellence, and OCI growth initiatives.

Requirements

• BS degree or equivalent in Computer Science, Engineering, Information Systems, Data Science, or related field; MS preferred.
• 7+ years in data engineering, analytics engineering, technical program management, software engineering, or related roles; 10+ years preferred.
• Proven experience designing and maintaining large-scale data pipelines, ETL/ELT frameworks, and cloud data platforms.
• Experience building BI solutions, executive dashboards, KPI frameworks, and operational reporting.
• Advanced SQL skills with large-scale datasets; strong data modeling and warehousing expertise.
• Cloud-native architectures, distributed systems, and OCI services; familiarity with Spark, Kafka, Airflow, Databricks, Snowflake, BigQuery, or OCI equivalents.
• Experience with BI tools such as Oracle Analytics Cloud, Tableau, Power BI, or Looker.
• Data governance, data quality, metadata management, and observability practices; self-service analytics and semantic data models.
• Proven ability to work with enterprise customers and strategic cloud initiatives; strong written and verbal communication skills.