Lead Data Engineer

3 days ago

Base Salary

$112k - $200k/yr

Responsibilities

Design, develop, and maintain scalable data pipelines and ETL/ELT frameworks for enterprise data and analytics workloads.
Build, model, transform, optimize, and manage workloads in Snowflake.
Develop reusable, production-grade data engineering components using Python and SQL.
Design and orchestrate complex workflows using Apache Airflow or comparable platforms.
Develop and support cloud-based data solutions using Microsoft Azure or comparable cloud technologies.
Translate business and technical requirements into scalable data solutions with cross-functional stakeholders.
Define technical designs, integration patterns, engineering standards, reusable frameworks, and best practices.
Implement data quality, monitoring, lineage, security, and operational controls across pipelines.
Troubleshoot production issues and improve performance, resiliency, scalability, and maintainability.
Conduct design and code reviews and provide technical guidance to data engineering teams.
Contribute to data platform modernization and adoption of modern engineering practices and technologies.

7+ years of professional experience in data engineering, data platforms, or related technology roles.
Strong hands-on expertise with Snowflake and star schema architecture.
Advanced programming experience with Python and SQL.
Strong experience with Apache Airflow or comparable workflow orchestration technologies.
Hands-on experience with Microsoft Azure or another major cloud platform such as AWS or GCP.
Strong experience designing ETL/ELT pipelines, data integration solutions, and enterprise data processing workflows.
Strong knowledge of SQL, data modeling, data warehousing, and performance optimization.
Experience designing scalable, reliable, maintainable solutions with production support.
Strong problem-solving, communication, and stakeholder-management skills.
Ability to work effectively across engineering, architecture, governance, business, and technology teams.
Experience with Databricks and Apache Spark is preferred.
Experience with Azure Data Factory and other Azure data services is preferred.
Knowledge of data governance, metadata management, lineage, data quality, and access-control frameworks is preferred.
Experience with enterprise data architecture, solution design, lakehouse, medallion architecture, and cloud-native data platforms is preferred.
Financial services or other highly regulated industry experience is a plus.

Medical, dental, and 401K plans are included in the employee benefits package.
Eligible employees may receive a discretionary bonus.
The role is hybrid with a current requirement to work in the office at least 50% of the time, or five out of every 10 business days.
The employer participates in the E-Verify program and offers accommodations for applicants with disabilities and other covered reasons.