Key skills
SQLPythonPySparkETL/ELT pipeline developmentEnterprise data modelingLakehouse architectureData warehousingData lakesData quality frameworksData monitoringData governanceAWS S3AWS IAMAWS GlueAWS Lake FormationSnowflakeDatabricksAzureGCPCloud-native data architecture designCloud securityIdentity managementAccess controlsTerraformInfrastructure-as-CodeGitCI/CD pipelinesDeployment automationPlatform monitoringAgileScrumKanbanSAFeMachine learning supportMLOpsGenAILarge Language ModelsAI-powered automationCloud migrationPlatform governanceCloud cost managementdbtTableauDatabricks Certified Data Engineer AssociateAWS Certified Cloud PractitionerSnowflake certificationsAzure Data Engineer AssociateTerraform AssociateStakeholder managementAIMachine LearningETLELTData EngineeringData WarehousingAnalyticsBusiness IntelligenceAWSS3IAMGlueIdentity ManagementCI/CDStakeholder ManagementMentoringCommunicationCloud Security
About this role
Enlyte is a company that combines innovative technology, clinical expertise, and human compassion to help people recover after workplace injuries or auto accidents. They are seeking a Senior Manager, Data Engineering to design and optimize data platforms that support enterprise analytics and AI initiatives, collaborating with various teams to deliver impactful data solutions.
Responsibilities:
- Design, build, and optimize modern data platforms that power enterprise analytics, reporting, machine learning, and AI initiatives
- Partner closely with business stakeholders, analytics teams, data scientists, architects, and engineering teams to deliver secure, governed, and reliable data products
- Design and maintain scalable data platforms and pipelines
- Develop and optimize cloud-based data architectures
- Automate infrastructure provisioning using Infrastructure-as-Code tools
- Gather requirements, troubleshoot complex technical issues, implement data quality controls, and improve platform governance and security
- Mentor engineers, conduct code reviews, evaluate emerging technologies such as GenAI and machine learning, and continuously improve data platform performance, reliability, and cost efficiency
Requirements:
- Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related technical field
- Equivalent combination of education and relevant work experience may be considered
- 10+ years of progressive experience in data engineering, analytics engineering, business intelligence, or related technical roles
- Proven track record designing and delivering enterprise-scale data platforms and analytics solutions
- Demonstrated ability to lead technical initiatives and influence architectural decisions
- Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders
- Experience operating in fast-paced, highly collaborative Agile environments
- Ability to independently drive projects from concept through implementation and support
- Strong problem-solving mindset with a focus on business outcomes, automation, and continuous improvement
- Advanced proficiency in SQL and Python
- Experience building and maintaining scalable ETL/ELT pipelines
- Strong expertise in PySpark and distributed data processing
- Experience designing enterprise data models and lakehouse architectures
- Deep understanding of data warehousing, data lakes, and modern data platforms
- Experience implementing data quality, monitoring, and governance frameworks
- Hands-on experience with AWS services such as S3, IAM, Glue, and Lake Formation
- Experience working with Snowflake, Databricks, Azure, or GCP
- Experience designing secure, scalable cloud-native data architectures
- Understanding of cloud security, identity management, and access controls
- Experience with Terraform or similar Infrastructure-as-Code tools
- Strong Git-based development practices
- Experience implementing CI/CD pipelines and deployment automation
- Knowledge of platform monitoring, optimization, and operational support
- Experience partnering with business stakeholders to translate requirements into technical solutions
- Ability to advise teams on best practices, architecture, and platform capabilities
- Experience working within Agile, Scrum, Kanban, or SAFe delivery frameworks
- Strong documentation, communication, and stakeholder management skills
- Master's degree in Computer Science, Information Systems, Data Science, or related field preferred
- Experience administering and scaling enterprise Snowflake environments
- Multi-cloud experience across AWS, Azure, and GCP
- Experience supporting machine learning, MLOps, or AI-enabled analytics platforms
- Hands-on experience with GenAI, Large Language Models (LLMs), and AI-powered automation solutions
- Experience leading cloud migrations from on-premises environments to cloud lakehouse architectures
- Experience developing platform governance, security, and cost-management strategies
- Experience building automated cloud provisioning frameworks and self-service data platforms
- Familiarity with dbt and modern analytics engineering practices
- Experience with Tableau or other business intelligence tools
- Proven ability to optimize cloud spending and improve platform efficiency
- Experience mentoring engineers, conducting code reviews, and establishing engineering standards
- Exposure to large-scale, high-volume data environments supporting enterprise analytics and data science workloads
- Databricks Certified Data Engineer Associate
- AWS Certified Cloud Practitioner or higher-level AWS certifications
- Snowflake certifications
- Azure Data Engineer Associate
- Terraform Associate or related cloud infrastructure certifications