Data Engineer
Location: Cincinnati, OH Hybrid 4 days a week onsite
Duration: 12 months + (High potential for extension)
Number of openings: 2
Job Description:
We are looking for a Data Engineer with at least 6+ years of IT experience. Any previous Banking/ Finance domain is a nice to have, but not required.
We are targeting experienced Data Engineers with a mixture of legacy (DataStage) ETL work and modern tech stack (Snowflake, dbt, etc). An ideal candidate will have hands on experience with both legacy DataStage and Snowflake & Dbt. These Core data engineers should also have a strong baseline of SQL and Python.
Top Desired Skills:
- SQL
- Snowflake
- DBT
- DataStage
- Strong Communication
These Data Engineers will support the enterprise customer master data conversion effort as part of integrating Comerica Bank s systems into Fifth Third s ECIF Customer Master Data Management environment. The role involves extracting, transforming, standardizing, and mapping customer and account data from Comerica s Snowflake environment into Fifth Third s enterprise Snowflake environment, then delivering curated views to downstream systems. 1. Customer Data Extraction & Analysis Extract customer and account data from Comerica s Snowflake environment.
Perform deep data analysis to understand data quality, lineage, structure, and anomalies.
Write SQL and procedural logic to pull, filter, and prepare data for downstream use. 2. Customer Conversion Pipeline Work Build, enhance, or maintain pipelines that convert Comerica data to Fifth Third s ECIF format.
Transform source data into standardized customer master data structures.
Support customer and account view generation for downstream system consumption. 3. ETL / ELT Development Create and maintain data extraction procedures using: Snowflake
dbt (SQL transformations)
IBM DataStage Move data from Comerica Fifth Third systems with reliable, repeatable pipelines. 4. Data Mapping & Standardization Map Comerica data fields to Fifth Third s ECIF field definitions and internal data models.
Implement standardization, cleansing, and conformance rules for customer and account attributes. 5. Downstream System Integration Deliver curated customer and account views to: ECIF (enterprise customer master)
Additional downstream systems (e.g., product accounting systems) Ensure downstream teams understand and can consume the resulting datasets.
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