This role pays less than
67%
of similar roles. Most pay
$135,675–$202,561
— the shaded band above.
At the midpoint, this role pays about
$156k
versus about
$169k
for comparable roles.
Based on 240 similar postings.
Employer
About Oracle
Oracle Corporation is a leading multinational technology company specializing in database software, cloud computing, and enterprise software.
Oracle currently has
242 open roles
on FindRole.
Listed pay typically runs
$102,300–$209,500
across 225 roles with salary data.
Senior Data Engineer joins the Capacity Data Analytics team to build a trusted data foundation, reporting platforms, and self-service analytics solutions. This role involves designing and delivering scalable data engineering solutions that transform complex operational data into actionable insights for Compute, Storage, Networking, and Data Center organizations. The individual will develop high-quality SQL-based data pipelines, curated datasets, and reusable data models while maintaining canonical datasets to inform long-term infrastructure planning. Key responsibilities include managing the full project lifecycle from requirements to production delivery, ensuring data quality, and communicating complex logic to diverse stakeholders. The role requires expertise in SQL, data modeling, ETL/ELT pipelines, and metadata management. Candidates must navigate ambiguous problems within the domain of cloud infrastructure capacity planning and operations analytics to improve efficiency through automated, data-driven solutions.
What does a Data Engineer earn?
Median $170950 from 215 postings across 56 companies.
Design, develop, and maintain scalable data engineering solutions and self-service analytics platforms.
Build high-quality SQL-based data pipelines, curated datasets, and reusable data models.
Develop and maintain canonical datasets to serve as trusted sources for strategic decision-making.
Translate complex business requirements from stakeholders into scalable technical data solutions.
Manage the full project lifecycle from initial requirements through production delivery.
Communicate data lineage, transformation logic, and business rules to both technical and non-technical audiences.
Improve data quality, governance, documentation, and engineering best practices.
Identify opportunities to automate manual processes and improve operational efficiency via data solutions.
What we're looking for
Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, or another quantitative discipline, or equivalent practical experience.
Strong experience developing complex SQL queries for large-scale analytical workloads.
Experience designing and implementing data warehouse solutions, data models, and ETL/ELT pipelines.
Strong understanding of data modeling, data quality, metadata, and data lineage concepts.
Experience delivering end-to-end data engineering solutions from requirements through production deployment.
Excellent analytical problem-solving skills to navigate ambiguous business problems.
Outstanding written and verbal communication skills for presenting technical concepts to both technical and non-technical audiences.
Experience supporting cloud infrastructure, capacity planning, or large-scale distributed systems (preferred).
Apache Spark
Scala
Java
Golang
Swift
Data Modeling
Data Pipelines
Big Data
OpenStack
AWS
Azure
Machine Learning
Feature Engineering
Knowledge Graphs
Data Governance
Azure
Python
SQL
Spark
Databricks
Azure Data Factory
Data Lake
Synapse Analytics
Azure Functions
ETL
Hadoop
NoSQL
Big Data
Data Modeling
Data Warehousing
DevOps
Real-Time Processing