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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** ALPHIDENT TECHNOLOGIES, INC. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $90,000.0 - $110,000.0 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Microsoft Azure, Cloud Database, Continuous Integration, Data Architecture, Data Dictionary, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Python (Programming Language), Machine Learning, Azure Machine Learning, Software Deployment, SQL Databases, Transact-SQL, Parquet, Data Logging, Data Processing, Large Language Models, Pandas, Pyspark, Data Analytics, Restful APIs, Azure Synapse Analytics, Software Version Control, Data Pipelines - **Published:** July 1, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ce3d630884620b8d ## About the Role Do you have experience in T-SQL?, Do you have a Bachelor's degree?, * Five (5) years of hands-on experience in each of the following: a. Maintaining SQL databases and conducting advanced operations in SQL and T-SQL. b. Designing, implementing, and maintaining ELT/ETL processes in cloud-based data analytics environments. 2. Three (3) years of hands-on experience in each of the following: A. Working in Azure Synapse and Azure Machine Learning, with the modern data stack. Certifications preferred (DP-203 or equivalent). B. Manipulating data in Python. Pandas required. PySpark/Polars preferred. Experience developing reusable, modular code preferred. Preferred experience: * Implementing pipelines and infrastructure using code-first approaches (Python SDK, CLI, REST APIs, or IaC tooling) * Implementing source control and CI/CD workflows * Demonstrated familiarity with AI coding assistants and LLM integration patterns, * Bachelor's (Required) ## Description * Design, implement, and maintain ELT/ETL pipelines for efficient processing of source data in Azure Synapse and Azure Machine Learning (using SDK V1 and SDK V2) * Review, maintain, and improve existing architecture and pipelines, including periodic audits to address bottlenecks, deprecated dependencies, and architecture drift. * Establish quality controls for maintaining all pipelines, and introduce error handling, logging mechanisms, and validation checks. * Incorporate source control for all pipelines and data analytics codebases to enable iterative code development while ensuring data architecture stability. * Optimize the ingestion, processing, and storage of a wide variety of datasets and data types, including modern columnar formats such as Parquet. * Design, implement, and maintain an efficient, secure, stable, and flexible data architecture that supports products and end-users, with all assets managed via source control. * Develop self-service capabilities for analysts to query and export data for investigations and audits. * Coordinate with data scientists to ensure the architecture efficiently supports machine learning algorithms and data pipelines in Azure Machine Learning. * Assist with data products by providing highly skilled and authoritative expertise on data engineering methods and best practices, including code-first development approaches and modern pipeline design patterns. * Develop robust standard operating protocols (SOPs) dictating the authoring, development, validation, publishing, execution, and monitoring of all data pipelines and assets in Azure environment. * Provide detailed documentation of the data architecture, including data dictionaries, ER diagrams, and pipeline process maps. * Maintain and expand the environment with additional datasets and services upon request, following a defined intake and testing process prior to production deployment. * Stay current with emerging AI tools relevant to data engineering, and contribute to exploratory efforts evaluating automation and LLM-assisted capabilities. ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Parquet, Delta, Iceberg & Ducklake - An introduction for developers](https://www.wearedevelopers.com/videos/100075-parquet-delta-iceberg-ducklake-an-introduction-for-developers) - [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) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are The Top Skills Required For Azure Developers?](https://www.wearedevelopers.com/magazine/77-what-are-the-top-skills-required-for-azure-developers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)