Lead the design, development, and maintenance of scalable data pipelines that integrate data from multiple sources into the Enterprise Data Platform.
Build pipelines and workflows as code using modern engineering practices including version control, code reviews, automated testing, and reusable components.
Define and implement patterns for continuous integration and continuous deployment for data pipelines including automated builds, tests, deployments, and environment promotion.
Partner with data scientists, analysts, and business teams to gather requirements and translate them into robust data solutions.
Build and optimize Structured Query Language queries and data transformations that support complex business use cases and analytics needs.
Design and manage data models and review them with business collaborators, data architects, and governance partners.
Establish data quality checks, validation, and troubleshooting practices to maintain accuracy, consistency, and trust in data products.
Monitor and optimize pipeline performance and reliability; implement observability including logging, metrics, and alerts; contribute to operational runbooks.
Drive automation to increase efficiency and reduce manual processes across platform operations.
Provide technical leadership through mentoring, peer reviews, and guidance on engineering standards.
Participate in Agile ceremonies to plan, estimate, and deliver work efficiently.
Create and maintain documentation for data workflows, transformations, standards, and operational procedures.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent experience.
Five to eight years of experience in data engineering or a related field.
Advanced proficiency in Structured Query Language for complex data transformation and analysis.
Hands-on experience with cloud-based data platforms such as Databricks or Snowflake.
Experience with ETL and ELT tools or frameworks such as Informatica, Talend, or dbt.
Strong proficiency in Python or PySpark for data processing and pipeline development.
Strong understanding of data modeling, database design principles, and building curated datasets for analytics and operational use cases.
Experience with DevOps practices including Git-based development, branching strategies, and code reviews.
Experience implementing continuous integration and continuous deployment for data pipelines and managing deployments across environments.
Familiarity with orchestration and workflow tools such as Databricks Workflows or Airflow is preferred.
Familiarity with Infrastructure as Code tools including Terraform or CloudFormation and containerization concepts is a plus.
Strong problem-solving skills, attention to detail, and skill in troubleshooting complex issues.
Strong communication skills and skill collaborating across technical and non-technical teams.

Benefits

Comprehensive healthcare plans
Wellness incentive program
Mental wellbeing support and fitness reimbursement
Bonus incentive opportunity
Matching 401(k) and stock purchase plan
Career development opportunities
Employee resource groups
On-demand learning and tuition reimbursement
Paid-time off
Parental leave
Flexible work-schedules
Volunteer opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Structured Query LanguagePythonPySparkETLELTdata modelingdatabase designcontinuous integrationcontinuous deploymentdata transformation

Soft Skills

problem-solvingattention to detailcommunicationcollaborationtechnical leadershipmentoring