Senior Manager, Data Engineering

Enlyte•UNAVAILABLE, UNAVAILABLE

5d•Remote

About The Position

We are seeking a highly skilled Senior Manager, Data Engineering to design, build, and optimize modern data platforms that power enterprise analytics, reporting, machine learning, and AI initiatives. The ideal candidate combines deep technical expertise across cloud platforms, data architecture, and automation with the ability to translate complex business requirements into scalable, high-impact solutions. This role will partner closely with business stakeholders, analytics teams, data scientists, architects, and engineering teams to deliver secure, governed, and reliable data products that drive measurable business value. The Senior Manager, Data Engineering will design and maintain scalable data platforms and pipelines, develop and optimize cloud-based data architectures, and automate infrastructure provisioning using Infrastructure-as-Code tools. They will collaborate with data scientists, analysts, architects, and business leaders to gather requirements, troubleshoot complex technical issues, implement data quality controls, and improve platform governance and security. The role also involves mentoring engineers, conducting code reviews, evaluating emerging technologies such as GenAI and machine learning, and continuously improving data platform performance, reliability, and cost efficiency across cloud environments including AWS, Snowflake, Azure, and Databricks.

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

Nice To Haves

Master's degree in Computer Science, Information Systems, Data Science, or related field
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

Responsibilities

Design and maintain scalable data platforms and pipelines.
Develop and optimize cloud-based data architectures.
Automate infrastructure provisioning using Infrastructure-as-Code tools.
Collaborate with data scientists, analysts, architects, and business leaders to gather requirements.
Troubleshoot complex technical issues.
Implement data quality controls.
Improve platform governance and security.
Mentor engineers.
Conduct code reviews.
Evaluate emerging technologies such as GenAI and machine learning.
Continuously improve data platform performance, reliability, and cost efficiency across cloud environments including AWS, Snowflake, Azure, and Databricks.