Senior Data Engineer

Position Summary

The Lead Data Solutions Engineer is responsible for designing, developing, implementing, and supporting enterprise-scale data platforms and solutions. This individual will collaborate closely with architects, engineers, analytics teams, business stakeholders, and technology partners to define best practices, establish governance standards, and drive adoption of modern data engineering methodologies. This role serves as a subject matter expert for enterprise data solutions, providing technical leadership, mentoring team members, and guiding strategic decisions related to data architecture, engineering, and platform optimization.

Key Responsibilities

• Lead the design, implementation, and governance of enterprise ETL/ELT pipelines utilizing modern cloud data technologies.
• Architect and review end-to-end data workflows from source systems through curated, analytics-ready datasets.
• Design scalable data architectures supporting ingestion, transformation, storage, and consumption layers.
• Develop reusable frameworks and patterns that improve maintainability, consistency, and efficiency across data engineering initiatives.
• Design and optimize Snowflake-based data platforms, including: Warehouse sizing and workload management, Performance optimization and tuning, Data security and access control frameworks, Cost management and resource governance, Establish best practices for enterprise-scale cloud data operations.
• Develop and govern data transformation frameworks using dbt.
• Create modular and reusable data models across staging, intermediate, and business-layer datasets.
• Implement documentation, testing, lineage tracking, and quality controls.
• Build standards that promote transparency, reliability, and self-service analytics.
• Design and support highly scalable data pipelines using SQL, Python, and PySpark.
• Build solutions that accommodate batch, streaming, and hybrid processing requirements.
• Oversee ingestion frameworks and ensure reliable movement of data across multiple environments and platforms.
• Implement Infrastructure as Code (IaC) practices using Terraform.
• Automate provisioning and management of data platform resources.
• Design CI/CD processes supporting: Automated deployments, Environment promotion strategies, Source control and release management, Code quality validation and testing.
• Establish standards for monitoring, observability, and data quality management.
• Ensure data platforms are reliable, scalable, and production-ready.
• Define service-level expectations and operational support processes.
• Drive continuous improvement initiatives focused on platform stability and performance.
• Lead architecture reviews, proofs of concept, and technology evaluations.
• Mentor and coach data engineers, providing technical guidance and best practices.
• Facilitate technical discussions and represent the data engineering function in cross-functional meetings.
• Influence enterprise-wide standards, governance models, and architectural direction.
• Partner with security, platform, and compliance teams to implement enterprise data governance requirements.
• Ensure solutions meet security, regulatory, and access-control standards.
• Balance governance objectives with engineering efficiency and delivery goals.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, Mathematics, Economics, or a related discipline, or equivalent practical experience.
• 7+ years of experience delivering enterprise-scale data engineering solutions.
• Demonstrated success leading large-scale data platform initiatives in cloud and/or hybrid environments.
• Strong experience building and supporting complex, high-volume data pipelines in production environments.
• Expertise in cloud data warehousing, data modeling, and modern analytics engineering practices.
• Advanced proficiency with SQL and strong hands-on experience with Python and/or PySpark.
• Experience with Snowflake, dbt, Fivetran, Terraform, and modern data ecosystem tools.
• Strong understanding of enterprise data warehousing concepts and architecture patterns.
• Experience designing, developing, testing, and deploying business intelligence and analytics solutions.
• Knowledge of CI/CD, Infrastructure as Code, and software development lifecycle best practices.
• Strong analytical, problem-solving, and systems-thinking capabilities.
• Excellent verbal and written communication skills with the ability to present complex technical concepts to both technical and non-technical audiences.
• Proven ability to lead initiatives, influence stakeholders, and drive outcomes with minimal oversight.
• Strong sense of ownership, accountability, and commitment to continuous improvement.

Preferred Experience

• Experience with AWS and/or Azure cloud environments.
• Exposure to Kafka or event-driven data architectures.
• Experience establishing data governance, metadata, and lineage frameworks.
• Background evaluating and implementing emerging data technologies.
• Experience mentoring engineering teams and contributing to enterprise architecture strategies.