> Markdown version of [/jobs/ext/3469281-telecommute-data-architect](https://www.wearedevelopers.com/jobs/ext/3469281-telecommute-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).

---

# TELECOMMUTE Data Architect

- **Company:** Hexaware Technologies
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Code Review, Databases, Continuous Integration, Data Architecture, Data Validation, Data Discovery, Data Infrastructure, Digital Assets, Java Database Connectivity, JSON, Python (Programming Language), Meta-Data Management, Microsoft SQL Server, Modular Design, MongoDB, OAuth, Role-Based Access Control, Power BI, Standard Sql, Microsoft SharePoint, Data Streaming, YAML, Snowflake, Git, Data Lakes, Pyspark, Data Management, Tools for Reporting, Restful APIs, Azure Synapse Analytics, Databricks
- **Published:** September 11, 2026
- **Apply:** https://www.dice.com/job-detail/c609d577-8a46-4405-bbde-b7fc42ec9ec3

## About the Role

Required Experience : Data Platform, Integration, Azure Databricks & Lakehouse Specialist, Strong hands-on background in PySpark, Medallion Architecture, Delta Live Tables, Unity Catalog, and MongoDB Atlas integration. Familiar with Databricks Genie for AI-assisted analytics. An effective technical leader who sets architectural direction, mentors teams, and drives delivery. Exposure to Power BI and reporting solutions is a plus, not a core requirement.

## Description

* Databricks: Medallion Architecture (Bronze/Silver/Gold), Delta Lake, Delta Live Tables (DLT), Auto Loader, Unity Catalog, Databricks Workflows, Databricks Apps, Databricks Genie
 * Languages: PySpark (expert), Python (PEP 8/PEP 20), SQL
 * Data Platforms: Azure Databricks, ADLS, Azure Synapse, Snowflake
 * Databases & Integration: MongoDB Atlas, SQL Server (JDBC), REST APIs, OAuth (SharePoint), cloudfiles
 * Governance: Unity Catalog - metastores, catalogs, schemas, RBAC, lineage, access policies
 * DevOps: Git, CI/CD for Databricks workflows and DLT pipelines, JSON/YAML config management
 * Reporting (nice-to-have): Power BI, DAX, Power Platform, * Designed and implemented end-to-end Lakehouse solutions on Azure Databricks across Bronze/Silver/Gold layers with schema enforcement and data quality checks
 * Delivered production-grade DLT pipelines and Auto Loader streaming ingestion from ADLS and external sources
 * Optimised PySpark jobs for performance and cost - partition tuning, caching, modular function-based code design

Unity Catalog & Governance

 * Implemented Unity Catalog as the foundational governance layer - metastores, catalogs, schemas, fine-grained RBAC, lineage, and audit controls
 * Standardised metadata management and data discovery across the platform

Data Architecture & Integration

 * Architected data flows from MongoDB Atlas, JDBC (SQL Server), REST APIs, and OAuth sources into the enterprise data platform
 * Established architectural standards for ingestion, transformation, and consumption layers; led architectural reviews across squads
 * Built configuration-driven (JSON/YAML) pipeline frameworks enabling scalable onboarding of new data sources

Databricks Genie

 * Familiar with Databricks Genie for enabling natural language querying of data assets and AI-assisted analytics for business users

Leadership

 * Mentored engineers on Databricks, PySpark, and Python best practices (PEP 8/PEP 20, naming conventions, modular design)
 * Conducted code reviews, enforced coding standards, and drove CI/CD adoption for Databricks workflows and DLT pipelines


## Related Videos

- [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering)
- [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94)
- [CI/CD with Github Actions](https://www.wearedevelopers.com/videos/856-ci-cd-with-github-actions)
- [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews)

## Related Articles

- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)
- [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)