Our client, a major software Development Company is seeking a Sr. Data Engineer. The Preferred candidate must have the following: Oracle Cloud, SQL, and non-federated relational data warehouse architecture to help complete the Map Builder capability as part of a larger Social Determinants of Health capability. The candidate will also need to be familiar with Census and CDC data.

High Level Tasks:

Redesign database using single partitioned FACT Table to improve system performance and data management efficiency.
Refactor Geography_DIM dataset structure to improve handling, performance, and utilization for mapping functionalities in the Map Builder tool.
Update how total deaths are coded in the CDC Mortality Data while ensuring the accuracy and reliability.
Deploy SDoH code to production instance supporting its first specific geography (implementation for a county in New Mexico).
Comprehensive review & validation of SDoH data readiness for product launch
Prepare the SDoH data for the initial launch of Map Builder 1.0, ensuring data accuracy and integrity.
Set up and configure ETL orchestration processes to support a data subscription model, enabling dynamic data access and updates.
Create a schema that facilitates a data subscription model for accessing the SDoH datamart, allowing for flexibility and scalability.


If you want to learn more, please contact Elite Technical for more information.

Required Skills

The skill set required for this role is below.

8+ Years experience in data warehousing in both Inmon and Kimball methodologies
8+ Years experience (expert level) in Oracle PL/SQL
Hands-on experience using Erwin Data Modeler for complex 3NF/Dimensional/Hybrid models.
Experience with metadata-driven solutions.
Hands-on experience working with VLDB and partitioning strategies.
Hands-on experience leveraging external API-s for augmenting DW content.
5+ Years experience working with cloud databases, preferably OCI and AWS.
Experience with a variety of database architectures including row stores, column stores, fully in-memory systems, etc.
Understanding of data architecture and performance optimization techniques including multi-node data distribution, partitioning/sharding, indexing, in-memory data structures, parallelization techniques, explain plans and/or tracing techniques, etc.
Detailed understanding of common data warehouse security requirements including dedicated/system users and roles, row-level security, masking and/or anonymization, SSO capabilities, etc.