> Markdown version of [/jobs/ext/4512222-maestro-data-architect](https://www.wearedevelopers.com/jobs/ext/4512222-maestro-data-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Maestro Data Architect - **Company:** PROLIM Global Corporation - **Location:** Hartford, CT, United States - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Cloud Computing, Cloud Database, Databases, Data Architecture, Extract Transform Load (ETL), Data Mart, Data Warehousing, Document-Oriented Databases, JSON, Job Scheduling, Python (Programming Language), Performance Tuning, Cloud Services, PL-SQL, SQL Databases, Extensible Markup Language (XML), Parquet, Scripting, Data Ingestion, Snowflake, Data Pipelines - **Published:** November 2, 2026 - **Apply:** https://www.careerbuilder.com/job-details/maestro-data-architect-hartford-ct--27ebd614-679f-4c0d-8e1b-3122040df181 ## About the Role DataArchitect Data Modeling Tool, Database Extract Transform and Load (ETL), Python Programming/Scripting Language, Cloud Computing, SQL (Structured Query Language), Amazon Web Services (AWS), Best Practices, Data Modeling, Data Mart, Performance Tuning/Optimization, Excellent analytical skills and strong understanding of SQL/PLSQL Job scheduling and orchestration using industry standard tools Skills/Technology: Hands-on experience with Cloud based Database, preferably Snowflake Excellent SQL / PL SQL skills Basic understanding on AWS Experience on orchestration / scheduling tool, preferably Tidal Experience on Python is an advantage Working experience on Data Warehousing and Modeling Basic understanding of Banking terminology ## Description Design, develop, and maintain ETL/ELT pipelines using Snowflake, SQL, Python, and/or cloud services preferably AWS Build and optimize data models (staging, warehouse, Datamarts) using best practices such as micro partitioning, clustering, and performance tuning. Develop and manage data ingestion workflows from structured and semi structured sources (JSON, Parquet, XML, APIs, etc.). Implementation of business requirements using Snowflake features such as Streams, Tasks, Time Travel, Zero Copy Cloning, and Snowpipe for automated data loads. Collaborate with Data architects, analysts, and business stakeholders to translate requirements into scalable technical solutions. Monitor and Troubleshoot pipelines for failures and issues ensuring data quality and integrity. Document data flows, transformations, and technical processes for maintainability and knowledge sharing