Data Engineering Manager

CrossCountry Mortgage, LLC•Cleveland, OH

About The Position

The Data Engineering Manager's role is to provide technical leadership for data engineering services including data modeling, data warehouse/lake architecture and administration, ETL pipelines. This role manages data engineers/developers and drives the technical solutions in support of various business services including analytics, reporting, AI analysis, modeling, data integration, and automation of manual data processes. The Data Engineering Manager is responsible for continuous improvement of data quality, processing costs, modeling, and services. The Data Engineering Manager will collaborate with a variety of stakeholders including product owners, analysts, functional leadership, vendors, and scrum/project resources to optimize solutions that are aligned with business needs.

Requirements

Bachelor’s degree in a related technical or business field.
5+ years of comparable work experience.
Experience with the following technologies and methods: SQL and data modeling
Data warehousing (Snowflake)
Process mapping/diagramming
Agile work management
Code management and release
AI code development
Excellent prioritization and problem-solving skills.
Customer service mindset.
Continuous improvement mindset.
Communication and teamwork skills.

Nice To Haves

Familiarity with transactional database management (PostgreSQL, MongoDB, etc.), preferred.

Responsibilities

Manage and mentor data engineers on projects, best practices, and professional growth.
Administer HR processes (hiring, performance, etc.) for reports.
Oversee ideation and execution of continuous improvement concepts.
Develop technical solutions for data platform implementation and services expansion.
Model data to suit business needs and efficient warehouse performance.
Oversee and implement ETL and reverse ETL pipelines/solutions integrated with various CCM application systems, external sources, S3 data lake, etc.
Develop efficient data assets for analytics, AI, and low latency queries.
Develop security access models.
Organize data pipelines/jobs and develop quality monitoring tools.
Resolve platform performance issues and bugs.
Execute platform configuration and performance tuning.
Monitor system details within the data warehouse, including stored procedures and execution time, and implement efficiency improvements.
Develop, implement, and enforce change control and testing processes.
Develop documentation for data architecture, models, semantic views, environment, etc. for team and end-user reference.
Develop and deploy end-user practices and tools for data extraction, queries, and data manipulation in accordance with business processes.
Support data governance, quality, privacy, and security initiatives and advocate for supported services.