Senior Data Engineer / W2

Stellent IT LLC; Jersey City (NJ); On-site; USD 150,000 - 190,000; Full time

Job summary

RITS in Jersey City, NJ is seeking a Senior Data Engineer with 8+ years of hands-on experience in enterprise data engineering. You will own Airflow DAG development, dbt Core modeling, and containerized workloads on Kubernetes/OpenShift to build scalable data pipelines for financial and accounting platforms.

The role requires strong Python skills, experience with performance tuning, observability, and collaboration with data architects and stakeholders to ensure high availability and reliable

Qualifications

• 8-10+ years of professional data engineering experience.
• Enterprise-scale data platforms in production.
• Expert Airflow design, scheduling, and performance tuning.
• Expert DBT Core modeling, testing, and implementation.
• Strong Python for data engineering and automation.

Responsibilities

• Design, develop, and maintain complex Airflow DAGs for pipelines.
• Lead dbt Core implementation, modeling, and CI/CD integration.
• Deploy and manage workloads on Kubernetes/OpenShift with scalability.

Skills

Airflow

DBT Core

Kubernetes

OpenShift

Python

Data modeling

ETL design

Performance tuning

Observability

Tools

dbt Core

Kubernetes

OpenShift

Job description

Job Title:- Senior Data Engineer

Tech Stack: Airflow, DBT Core, Kubernetes/OpenShift

Location: Onsite 3 days/week 185 Hudson St #1150, Jersey City, NJ 07311 (Looking only Local with local DL)

Proper LinkedIn + Original resume only

w2 Only

Title: Senior Data Engineer Airflow, DBT Core, Kubernetes/OpenShift Jersey City, NJ and RITS

Role Overview

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.

Key Responsibilities & Outcomes 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

Qualifications & Requirements Required:

• 8-10+ 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
• 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

Preferred:

• 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)