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Egis

Senior Data Engineer

Egis

Senior Data Engineer responsible for designing and optimizing Azure-based data architectures. Supports advanced analytics and machine learning initiatives at Immersion Consulting, LLC.

Posted 7/31/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing Azure-based data architectures and ELT/ETL pipelines, with a strong focus on optimizing data ingestion, processing, and storage. Proficient in collaborating with data scientists to support machine learning initiatives and ensuring efficient data access for analytics.

Highest-signal resume keywords
Azure Data ArchitectureELT/ETL Pipeline DevelopmentSQL Database ManagementAzure Synapse AnalyticsPython and Pandas Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data Architecture DesignPipeline OptimizationAdvanced SQL/T-SQL OperationsData ModelingData Ingestion and Processing
Tools & Technologies
Azure Data Lake StorageAzure Machine LearningSource Control SystemsData Validation FrameworksOperational Standards
Industry Keywords
Cloud-Based Data SolutionsFraud Detection InitiativesMachine Learning OperationsData Pipeline MonitoringStandard Operating Procedures

Tech Stack

Tools & technologies
AzureCloudETLPandasPythonSQL

About the role

Key responsibilities & impact
  • The Senior Data Engineer is responsible for designing, implementing, maintaining, and optimizing a cloud-based data architecture and data pipeline ecosystem.
  • The position supports advanced analytics, machine learning operations, fraud detection initiatives, and investigative activities by delivering scalable, secure, and sustainable Azure-based data solutions.
  • The Senior Data Engineer develops and maintains modern ELT/ETL pipelines, data models, source-controlled environments, and operational standards that enable efficient data ingestion, processing, storage, and access.
  • Design, implement, and maintain scalable Azure-based data architecture supporting audits, investigations, and fraud analytics.
  • Develop, optimize, and sustain ELT/ETL pipelines within Azure Synapse Analytics and Azure Machine Learning environments.
  • Migrate and integrate large-scale datasets into Azure Data Lake Storage (ADLS).
  • Establish source control, version management, and development standards across data engineering assets.
  • Implement pipeline monitoring, validation, logging, and error-handling frameworks.
  • Design and maintain data models, data dictionaries, entity relationship diagrams, and architectural documentation.
  • Optimize ingestion, transformation, storage, and retrieval performance across diverse data sources and formats.
  • Develop self-service data access capabilities for analysts and investigators.
  • Collaborate with Data Scientists to ensure infrastructure effectively supports machine learning and AI initiatives.
  • Author and maintain Standard Operating Procedures (SOPs) governing data pipeline development, deployment, and monitoring.
  • Evaluate emerging AI-enabled engineering tools and LLM-assisted automation capabilities.
  • Recommend and implement architectural improvements that increase efficiency, reliability, security, and cost effectiveness.

Requirements

What you’ll need
  • Bachelor's degree in Data Engineering, Computer Science, Data Science, Machine Learning, Mathematics, or related discipline; or 5 years of relevant applied experience.
  • Five or more years of experience maintaining SQL database environments and performing advanced SQL/T-SQL operations.
  • Five or more years of experience designing and maintaining cloud-based ELT/ETL solutions.
  • Three or more years of experience working with Azure Synapse Analytics and Azure Machine Learning.
  • Three or more years of experience developing data solutions using Python and Pandas.
  • Experience supporting modern data platforms and cloud-native analytics architectures.
  • Demonstrated expertise in data architecture design, pipeline optimization, and operational support.

Benefits

Comp & perks
  • Health insurance
  • 401(k) matching
  • Flexible work hours
  • Paid time off
  • Remote work options