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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Microsoft - **Location:** Redmond, WA, United States - **Experience:** Expert - **Salary:** $145,600.0 - $156,000.0 - **Contract:** Temporary to permanent - **Skills:** .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Big Data, Cloud Computing, Cloud Database, Cloud Engineering, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Profiling, Data Systems, Relational Databases, Software Debugging, Distributed Computing Environment, Distributed Data Store, Event-Driven Programming, Monitoring of Systems, Python (Programming Language), Language Modeling, NoSQL, Operational Databases, Performance Tuning, Software Engineering, Data Streaming, Systems Integration, Data Storage Technologies, Large Language Models, Concurrency, Apache Spark, Spring-boot, Indexer, Backend, Event Driven Architecture, Data Lakes, AI Platforms, Storage Technologies, Deployment Automation, Apache Kafka, Codebase, Data Pipelines, Automation Anywhere, Data Generation - **Published:** October 13, 2026 - **Apply:** https://www.careercircle.com/jobs/all/all/usa/wa/redmond/4b7b80da-b15b-4586-b673-e56f3c1c6c97 ## About the Role * 5+ years of professional software engineering, backend engineering, data engineering, or equivalent experience building and supporting production systems. * Strong backend programming experience using languages and frameworks such as Python, .NET, Spring Boot, or comparable backend technologies. * Demonstrated experience designing maintainable backend services, modular components, APIs, and automated tests. * Deep hands-on experience designing, building, scaling, and operating data generation, ETL, and distributed data-processing workflows. * Experience building and maintaining cloud-based data pipelines and large-scale data systems. * Strong working understanding of Large Language Models (LLMs), including model capabilities, limitations, common failure modes, prompting, data quality, evaluation, and model behavior. * Experience integrating or evaluating LLM-based workflows, AI platforms, frameworks, or APIs. * Working knowledge of orchestration, concurrency, asynchronous execution, distributed processing, streaming or event-driven architectures, and resilient failure recovery. * Strong knowledge of large-scale data storage, modeling, and retrieval, including partitioning, indexing, access patterns, and storage architecture trade-offs. * Strong quantitative analysis, debugging, and troubleshooting skills with the ability to validate data and system behavior and make evidence-based engineering decisions. * Practical cloud engineering experience, including building, deploying, monitoring, and supporting scalable backend or data systems. * Experience developing automated testing, data-quality checks, validation frameworks, or similar engineering quality controls. * Ability to independently own technical work from initial assessment and design through implementation, deployment, documentation, and ongoing support. * Strong technical communication and documentation skills with demonstrated ability to collaborate effectively with cross-functional teams. * Experience with Apache Spark, Kafka, or equivalent distributed-processing, streaming, or event-driven technologies is preferred. * Experience with relational databases, NoSQL databases, cloud object storage, or data lake architectures is preferred. * Experience with system observability, performance optimization, and deployment automation is preferred., Individual compensation offered for this position within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, geographic location, internal equity, and other pertinent job-related factors. ## Description NoSQL Research Indexing Debugging Automation Innovation Resilience Data Lakes Scalability Reliability Spring Boot Data Quality Apache Spark Data Storage Apache Kafka Observability Data Modeling Collaboration .NET Framework Failure Causes Data Profiling Data Pipelines Object Storage Test Automation Data Engineering Cloud Engineering Workflow Management Data Infrastructure Software Engineering Relational Databases Engineering Research Storage Architecture Operational Databases Quantitative Analysis Distributed Data Store Technical Documentation Technical Communication Large Language Modeling Event-Driven Programming Authorization (Computing) Synthetic Data Generation Employee Assistance Programs Extract Transform Load (ETL) Python (Programming Language) Troubleshooting (Problem Solving) Artificial Intelligence Infrastructure Application Programming Interface (API), We are seeking an experienced Senior Backend & Data Engineer to design, build, and evolve scalable backend systems and data infrastructure supporting next-generation AI-powered experiences. This is a hands-on engineering role focused on synthetic data generation, cloud-based data pipelines, distributed processing, large-scale storage, data quality, and LLM-enabled workflows., The successful candidate will take ownership of technical initiatives from assessment and architecture through implementation, testing, deployment, and ongoing support. This role requires strong software engineering fundamentals, deep experience building production data systems, and a practical understanding of Large Language Models (LLMs) and AI workflows. The ideal candidate is comfortable working within existing codebases, navigating ambiguity, evaluating technical trade-offs, and collaborating closely with engineering, research, design, and product partners. This is a fully remote position operating Monday through Friday, 40 hours per week, with availability required during a portion of Pacific Time business hours., * Design, build, and evolve synthetic data-generation systems that transform structured inputs and contextual information into coherent, traceable, and reproducible datasets. * Develop and maintain scalable backend services, data pipelines, ETL workflows, and distributed data-processing systems. * Build and operate orchestration frameworks utilizing concurrency, asynchronous execution, resilient failure handling, and recovery mechanisms. * Design and optimize data models, storage architectures, retrieval strategies, partitioning approaches, indexing, and access patterns for large-scale datasets. * Integrate Large Language Models into backend and data workflows using AI platforms, frameworks, APIs, and related technologies. * Develop evaluation and validation frameworks that define quality criteria, automate data checks, and measure the quality and consistency of system outputs. * Apply quantitative analysis and data profiling techniques to evaluate system performance, investigate issues, and improve reliability. * Build, deploy, monitor, troubleshoot, and maintain cloud-based backend and data systems with an emphasis on scalability, operational reliability, and cost efficiency. * Work within existing codebases to identify technical gaps, improve architecture, resolve defects, and implement maintainable solutions. * Evaluate engineering trade-offs and translate evolving or ambiguous requirements into practical technical solutions. * Create and maintain automated tests, technical documentation, operational guidance, and engineering standards. * Collaborate with engineers, researchers, designers, strategists, and product partners to prototype, evaluate, and deliver innovative AI-powered solutions. * Communicate technical progress, risks, dependencies, design decisions, and recommendations to engineering and cross-functional stakeholders.