Capgemini
Insurance
FBSDataEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“FBS Data Engineer at Capgemini. Skills: Data engineering, Data pipelines, Cloud data. Acquire data. Curate data”
Industry & Context.
Root cause analysis
What They're Looking For.
Must Have
04 to 06 years total experience, 04 to 06 years relevant experience, Intermediate ETL pipelines, Intermediate Agile team developer role familiarity
Nice to Have
4-6 Years AWS DBT Intermediate, 4-6 Years AWS S3 Intermediate, 4-6 Years AWS GLUE Intermediate, 4-6 Years AWS Lambda Intermediate, 4-6 Years Snowflake Intermediate
What You'll Do.
Translate business requests
Develop data products
Implement data products
Practice code management
Integrate with guidelines
Produce data building blocks
Create business user access methods
Map data to common model
Validate data content
Break down technical stories
Estimate work for sprints
Develop data pipelines
Maintain data pipelines
Build API integrations
Optimize data for reporting
Apply data governance concepts
Execute mid-size projects
Develop data project plans
Execute data project plans
How You'll Work.
Team & Collaboration
Agile team developer role
Process & Methodology
Agile
Full Job Description
1. Responsible for acquiring, curating, and publishing data both on prem and in the cloud for analytical or operational uses for basic to moderate scenarios. 2. Ensures the data is in a ready-to-use form that creates a single version of the truth across all data consumers, including business/technology users, reporting and visualization specialists and data scientists with coaching and support. 3. Utilizes skills to translate business analytic requests/requirements into design, development, testing, deployment, and production maintenance tasks. 4. Works with various technologies from big data, relational and non-relational databases, cloud environments, different programming languages and various reporting tools and is familiar with a few but requires training for some Essential Job Functions 1. Consults on data projects of intermediate complexity, with limited coaching and guidance, to understand design requirement and existing business processes to lead in the design, development, and implementation of data products. 2. Practices code management and integration with current architectural and data governance guidelines with reduced oversight required of leadership. 3. Produces data building blocks, data models, and data flows for varying client requests such as dimensional data, standard and ad hoc reporting, data feeds, dashboard reporting, and data science research and exploration with limited assistance and coaching. 4. Creates business user access methods to structured and unstructured data. Utilizes techniques such as mapping data to a common data model, natural language processing, transforming data as necessary to satisfy business rules, AI, statistical computations, and validation of data content. 5. Translates business data stories into a technical story breakdown structure and work estimates for a schedule or planned agile sprint. 6. Projects are of increasing complexity but require less coaching and assistance from leadership. Develops and maintains mode
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