Senior Data Engineer

DISHER

. Design, build, test, deploy, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and related services .

Posted 10/8/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and managing scalable ETL/ELT pipelines using Azure services, with a strong focus on data modeling and AI/ML integration. Proficient in ensuring data quality, governance, and compliance while mentoring junior engineers and collaborating with cross-functional teams.

Highest-signal resume keywords

Azure Data FactoryAzure Synapse AnalyticsAzure DatabricksSQLETL/ELT Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingStar SchemaNormalizationData VaultAI/ML Data WorkflowsGitCI/CDTestingDeployment PracticesData Governance

Soft Skills

MentorshipCollaborationTechnical Guidance

Tools & Technologies

Azure Data Lake Storage Gen2Azure OpenAI ServiceCognitive ServicesVector DatabasesMicrosoft Fabric

Industry Keywords

Data EngineeringData ArchitectureData QualityData LineageCompliance

Tech Stack

Tools & technologies

AzureETLPythonSQLVault

About the role

Key responsibilities & impact

Design, build, test, deploy, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and related services
Architect and manage Azure-based data lake and data warehouse solutions, including ADLS Gen2, Synapse, and Microsoft Fabric
Develop and optimize dimensional, star-schema, normalized, and data-vault models for business intelligence and AI/ML use cases
Partner with AI Engineers to prepare and deliver trusted data for model training, feature stores, retrieval-augmented generation, and inference pipelines
Integrate Azure OpenAI, Azure AI Foundry, Cognitive Services, embeddings, vector databases, and related AI services into data workflows and applications
Contribute to data architecture for AI-driven applications used by internal and external users
Establish and maintain controls for data quality, governance, lineage, access, security, and compliance
Monitor, troubleshoot, and optimize pipeline reliability, performance, and Azure cost efficiency
Translate business and product requirements into practical technical data solutions with Analytics, Product, Engineering, and other stakeholders
Provide technical guidance and mentorship to junior data engineers through code reviews, design discussions, and knowledge-sharing sessions
Help define and enforce data engineering standards, coding conventions, reusable patterns, and best practices
Assist with scoping and estimating data engineering work for sprint planning and project roadmaps

Requirements

What you’ll need

4-6 years of experience as a Data Engineer or in a comparable data infrastructure role
Strong hands-on experience with Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Azure Data Lake Storage Gen2
Proficiency in SQL and at least one programming language; Python is preferred
Experience building and orchestrating scalable ETL/ELT pipelines
Experience applying data modeling principles, including star schema, normalization, and data vault
Experience supporting model training, feature stores, inference pipelines, or similar AI/ML data workflows
Familiarity with Azure OpenAI Service or comparable LLM integration patterns, including RAG pipelines, embeddings, and vector databases
Experience with Git, CI/CD, testing, and deployment practices
Understanding of data security, compliance, governance, quality, and lineage principles
Experience mentoring engineers, leading small technical initiatives, or serving as a technical point of contact
Formal people-management experience is not required

Benefits

Comp & perks

Supportive, collaborative environment
Work-life balance
Professional development opportunities
Technical and personal advancement opportunities
Distributed workplace / remote work arrangement