Sr. Data Engineer : Alpine Capital Research
ShiftCode AnalyticsSt Louis, MO, United Statesjob_description.job_card.30_days_ago
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- serp_jobs.job_card.full_time
job_description.job_card.job_descriptionDesigning, developing, and maintaining data pipelines and warehousing solutions. Key tasks will include API integration, ETL development, data modeling (Star Schema or Snowflake Schema), and supporting Power BI reporting. Collaborate with internal project teams to ensure data accuracy, integrity, and structured organization for business intelligence. Azure Synapse Analytics Handles data warehousing and large-scale analytics workloads. Azure Data Lake Supports both structured and unstructured data. Scalable foundation for analytics and data integration. Azure Key Vault Ensures secure access across both environments. Azure DevOps Manages data pipeline lifecycle and component delivery. Apache Spark Notebooks Used for interactive data exploration, transformation, and analytics. Azure Integration Runtime Enables transformations across network boundaries within Synapse or Data Factory. Metastore Data Warehouse Maintains schema definitions, and table metadata ARM Template (Azure Resource Manager) Enables consistent provisioning of Synapse, Data Lake, Key Vault, and other resources across environments.
Interview : Video ( Prefer local candidates who can go for F2F interview but open to non local for video interview )
Visa : USC, GC, GC EAD, H4 , L2
Description :
- Initial Project : Migrate existing Python data extraction tool and codebase to Alpine Data Lake, set up API extraction in Synapse, establish daily ETL for updates, and update Power BI reports to use data lake source. [Alpine Strategic Credit (ASC)]
- Subsequent Project : Extract and migrate 20+ years of historical data from Portfolio system to a data warehouse, set up real-time API for continuous data feed from Tamarac, establish daily ETL for updates, and enable improved Power BI reporting. [Alpine Private Wealth (APW)]
- Both projects involve ensuring data accuracy and integrity, structuring data for seamless integration into the data warehouse and Power BI.
- Data modeling will utilize a Star Schema or Snowflake Schema approach.
Responsibilities :
Tech stack :
Two separate environments (e.g., Development and Production).
Centralized storage layer.
Manages secrets, encryption keys, and certificates.
CI / CD pipelines for automated builds and deployments.
Deployed in both environments.
Facilitates secure and scalable data movement.
Centralized metadata repository.
Defines and automates infrastructure deployment.
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Sr Data Engineer • St Louis, MO, United States
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