> Markdown version of [/jobs/ext/3191039-data-architect](https://www.wearedevelopers.com/jobs/ext/3191039-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). --- # Data Architect - **Company:** Realign Llc - **Location:** Edison, NJ, United States - **Salary:** $146,000.0 - **Contract:** Permanent contract - **Skills:** Data Analysis, Continuous Integration, Data Architecture, Data Validation, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Mapping, Data Security, Data Warehousing, Software Design Patterns, DevOps, Financial Software, Apache Hive, Role-Based Access Control, SAP (Applications), Transaction Data, Data Ingestion, Azure Data Factory, Sql Optimization, Microsoft Fabric, Data Lakes, Pyspark, Star Schema, Data Pipelines, Databricks - **Published:** September 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9e4ec3381b0c14b0 ## About the Role Must Have Technical/Functional Skills * Strong hands-on experience in Microsoft Fabric / Azure Data Engineering stack o Lakehouse, OneLake, Pipelines, Notebooks o Azure Data Factory / ADLS / Databricks * Strong knowledge of: o Data Lake architecture & design patterns o ETL/ELT frameworks and pipeline orchestration o PySpark / Spark SQL / Advanced SQL o Data Lake / Lakehouse / DWH concepts o Data modeling (Star Schema, Fact/Dimension design o Data security (row/column level security concepts * Strong experience in Data Lake / Data Warehouse implementations as an Architect * Experience in data governance, security & access control (RBAC, Purview, etc.) * Proven experience in enterprise-scale data platform design * Experience in CI/CD & DevOps for Data Engineering * Hands-on experience working with SAP and multiple ERP systems (Sales & Finance modules preferred) for data ingestion * Good understanding of: o Data mapping (source-to-target at attribute level) o ERP data structures (master & transactional data) o Data analysis and data validation techniques