Senior Database Engineer

This is a fully remote position, open to applicants in India.

📋 Description

• Design, develop, enhance, and maintain modern Microsoft data platforms utilizing Azure and Microsoft Fabric.

• Take ownership of cloud data engineering, enterprise data warehouse and lakehouse architecture, data integration, semantic consumption, and AI-ready data products.

• Architect scalable Fabric Lakehouse, Fabric Warehouse, and hybrid warehouse systems that include raw, standardized, curated, and consumption-ready layers.

• Create dimensional models, star schemas, facts, dimensions, data marts, semantic-ready datasets, and reusable data products.

• Develop production ETL/ELT processes using Fabric Data Factory, Azure Data Factory, SQL, Python, PySpark, notebooks, APIs, files, and both event-driven and batch integration patterns.

• Engineer solutions aligned with OneLake, including shortcuts, pipelines, notebooks, SQL endpoints, Power BI semantic models, and Direct Lake patterns.

• Establish governed data foundations for generative AI, copilots, agents, search, and analytics through Microsoft Foundry or Azure OpenAI, Azure AI Search, and enterprise data sources.

• Design and execute RAG workflows that encompass ingestion, chunking, metadata, embeddings, vector/hybrid retrieval, grounding, prompt design, citations, and evaluation.

• Assess LLM solution quality, grounding, latency, cost, content safety, data leakage risk, and business suitability prior to production deployment.

• Utilize Copilot in Microsoft Fabric, GitHub Copilot, Microsoft 365 Copilot, Copilot Studio, or authorized enterprise copilots to expedite development and enhance user-facing experiences.

• Implement responsible AI practices, including human review, access controls, privacy protections, prompt and model testing, auditability, and monitoring.

• Establish data quality, lineage, observability, reconciliation, validation, security, and governance measures throughout the engineering lifecycle.

• Employ Git, pull requests, automated testing, CI/CD, environment promotion, and Infrastructure as Code for data pipelines, notebooks, database objects, and platform configuration.

• Optimize workloads for performance, scalability, reliability, and cost-efficiency across SQL, Spark, Fabric capacity, storage, pipelines, and semantic models.

• Collaborate with BI, analytics, application engineering, DBAs, security, infrastructure, and business stakeholders to design end-to-end solution architecture.

• Provide technical leadership through code and design reviews, reusable templates, technical documentation, and mentoring.

⛳️ Requirements

• At least 7 years of professional experience in data engineering, database engineering, data warehousing, or a closely related field, with senior-level design ownership.

• Practical, hands-on experience with Azure data services and Microsoft Fabric or similar Microsoft cloud lakehouse/warehouse technologies.

• Proficient in SQL with a solid understanding of relational design, dimensional modeling, data warehousing, data marts, and performance engineering.

• Experience in building and managing ETL/ELT pipelines using SQL, Python or PySpark, notebooks, APIs, files, orchestration, and incremental processing methodologies.

• Hands-on experience in AI/LLM, having implemented at least one Copilot, chatbot, agent, RAG, intelligent search, or LLM-enabled data solution.

• Knowledge in prompt engineering, grounding, embeddings, vector or hybrid retrieval, model evaluation, responsible AI practices, security, and observability.

• Proficient with Git, pull requests, automated testing, CI/CD, environment promotion, and standardized deployment practices.

• Strong understanding of identity, cloud security, data governance, privacy, data quality, lineage, and operational support.

• Demonstrated ownership, sound judgment, attention to detail, and disciplined follow-through.

• Excellent written and verbal communication skills, capable of engaging both technical and non-technical stakeholders.

• Ability to mentor team members, lead design or incident reviews, and document repeatable standards.

• Commitment to security, data privacy, responsible technology use, and a culture of continuous learning.

🏝️ Benefits

• Equal opportunities for all prospective employees.

• Zero tolerance for discrimination.

• Inclusive hiring practices regardless of age, sex, race, ethnicity, disability, and other factors unrelated to job performance.