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Data Architech

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Onsite

Data Architech

Qloron Pvt Ltd

Data Architech

Skills

SQL QueriesSnowflakeData AnalysisPower BI

1 month ago

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Important information


Contract type:

Permanent

Location:

Pune Division, Maharashtra, India

Work mode:

Onsite

Published on:

25 September 2026

What they need


QT-MA-09-746

We are looking for an experienced Data Architect / SQL Server Data Engineer to design and enforce an on-premises SQL-first Medallion Architecture (Bronze, Silver, Gold) using SQL Server 2025 Standard Edition . The role will focus on building robust, metadata-driven ingestion frameworks for a greenfield NetSuite ERP , leveraging SuiteAnalytics Connect and SuiteQL , with particular emphasis on engineering reliable hard-delete detection mechanisms. The candidate will develop custom T-SQL frameworks for data quality, matching, and survivorship within the Silver layer to address the deprecation of Microsoft DQS and MDS, while designing Kimball-style dimensional models , including Star and Snowflake schemas, optimized for downstream reporting and analytics through Power BI Service and the Enterprise On-Premises Data Gateway . The role also requires evaluating and implementing high-volume ingestion solutions, balancing metadata-driven SSIS pipelines with hybrid SaaS platforms such as Fivetran , while operating within strict network security boundaries. The ideal candidate should have expert knowledge of SQL Server internal architecture , including Resource Governor, temporal tables, and columnstore indexes for analytical workloads on Standard Edition, along with proven experience migrating legacy ERP historical data and integrating it with new cloud ERP operational data in an on-premises data warehouse. Strong knowledge of the NetSuite data model, ODBC drivers, SuiteAnalytics Connect, and connection concurrency limits is required, as well as mastery of Power BI semantic model design, DAX, Row-Level Security (RLS), and gateway capacity planning . The candidate should also have strong experience designing automated, idempotent ETL/ELT frameworks that clearly separate extraction logic from business transformations and ensure reliable, maintainable, and scalable data pipelines.

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