Title: Data Engineer
Location: Jersey City, NJ Hybrid
Contract to Permanent
Interview mode: 1st round Virtual and 2nd round In-person
Must have:
- Python
- Apache Airflow/DBT
- Communication, both written & verbal
- Kubernetes
- OpenShift
- 8+ years of experience
Project:
- This role is critical to building, operating, and optimizing scalable data pipelines that support financial and accounting platforms, including enterprise system migrations and high-volume data processing workloads. They are implementing a new accounting system and merging data warehouses so they need a strong data engineer to come and design/engineer for optimization and streamline process automation
- Looking for someone to support the business and think strategically on how to improve what they have today, as well as do the administration hands-on work to drive the roadmap
-this person will likely partner with the BBH cloud engineering team that supports OpenShift for the firm but they are limited to how much help, capital partners (this division) has their own business requirements vs the rest of the firm
Job Description
- We are seeking a highly skilled Senior Data Engineer with 8+ years of hands-on experience in enterprise data engineering, including deep expertise in Apache Airflow DAG development, dbt Core modeling and implementation, and cloud-native container platforms (Kubernetes / OpenShift).
- This role is critical to building, operating, and optimizing scalable data pipelines that support financial and accounting platforms, including enterprise system migrations and high-volume data processing workloads.
- The ideal candidate will have extensive hands-on experience in workflow orchestration, data modeling, performance tuning, and distributed workload management in containerized environments.
Performance & Reliability
- Monitor and tune end-to-end pipeline performance across Airflow, dbt, and data platforms
- Identify bottlenecks in query execution, orchestration, and infrastructure
- Implement observability solutions (logs, metrics, alerts) for proactive issue detection
- Ensure high availability, fault tolerance, and resiliency of data pipelines
Collaboration & Governance
- Work closely with data architects, platform engineers, and business stakeholders
- Support financial reporting, accounting, and regulatory data use cases
- Enforce data engineering standards, security best practices, and governance policies
Required Skills & Qualifications:
Experience
- 10+ years of professional experience in data engineering, analytics engineering, or platform engineering roles
- Proven experience designing and supporting enterprise-scale data platforms in production environments
Must-Have Technical Skills
- Expert-level Apache Airflow (DAG design, scheduling, performance tuning)
- Expert-level DBT Core (data modeling, testing, macros, implementation)
- Strong proficiency in Python for data engineering and automation
- Deep understanding of Kubernetes and/or OpenShift in production environments
- Extensive experience with distributed workload management and performance optimization
- Strong SQL skills for complex transformations and analytics
Cloud & Platform Experience
- Experience running data platforms on cloud environments
- Familiarity with containerized deployments, CI/CD pipelines, and Git-based workflows
Preferred Qualifications
- Experience supporting financial services or accounting platforms
- Exposure to enterprise system migrations (e.g., legacy platform to modern data stack)
- Experience with data warehouses (Oracle)
Job Responsibilities
Data Pipeline & Orchestration
- Design, develop, and maintain complex Airflow DAGs for batch and event-driven data pipelines
- Implement best practices for DAG performance, dependency management, retries, SLA monitoring, and alerting
- Optimize Airflow scheduler, executor, and worker configurations for high-concurrency workloads
DBT Core & Data Modeling
- Lead DBT Core implementation, including project structure, environments, and CI/CD integration
- Design and maintain robust DBT models (staging, intermediate, marts) following analytics engineering best practices
- Implement DBT tests, documentation, macros, and incremental models to ensure data quality and performance
- Optimize DBT query performance for large-scale datasets and downstream reporting needs
Cloud, Kubernetes & OpenShift
- Deploy and manage data workloads on Kubernetes / OpenShift platforms
- Design strategies for workload distribution, horizontal scaling, and resource optimization
- Configure CPU/memory requests and limits, autoscaling, and pod scheduling for data workloads
- Troubleshoot container-level performance issues and resource contention
Performance & Reliability
- Monitor and tune end-to-end pipeline performance across Airflow, DBT, and data platforms
- Identify bottlenecks in query execution, orchestration, and infrastructure
- Implement observability solutions (logs, metrics, alerts) for proactive issue detection
- Ensure high availability, fault tolerance, and resiliency of data pipelines
Collaboration & Governance
- Work closely with data architects, platform engineers, and business stakeholders
- Support financial reporting, accounting, and regulatory data use cases
- Enforce data engineering standards, security best practices, and governance policies
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