Data Engineer

Intone Inc; Jersey City (NJ); On-site; USD 120,000 - 160,000; Part time

Job summary

Intone Inc. in Jersey City, NJ seeks a Senior Data Engineer for a 6-month contract to design, build, and optimize enterprise-scale data pipelines using Airflow, dbt, and cloud-native infrastructure.

You will lead workflow orchestration, data modeling, and platform automation while collaborating with data architects and business stakeholders on financial and accounting data use cases.

Qualifications

• 10+ years of professional experience in data engineering or related roles.
• 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.
• 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.
• Experience running data platforms on cloud environments.
• Familiarity with containerized deployments, CI/CD pipelines, and Git-based workflows.

Responsibilities

• Design, develop, and maintain Airflow DAGs for batch and event-driven pipelines.
• Improve DAG performance, dependency management, retries, SLA monitoring, and alerting.
• Tune Airflow scheduler, executor, and worker configurations for high concurrency.
• Lead dbt Core implementation, including structure, environments, and CI/CD.
• Build robust dbt models (staging, intermediate, marts) following analytics engineering practices.
• Develop dbt tests, documentation, macros, and incremental models for quality.
• Optimize dbt query performance for large-scale datasets and reports.
• Deploy data workloads on Kubernetes/OpenShift and manage resources.
• Plan workload distribution, horizontal scaling, and resource optimization.
• Configure CPU/memory requests and autoscaling; manage pod scheduling.
• Troubleshoot container performance and contention issues.
• Monitor end-to-end pipeline performance across Airflow, dbt, and platforms.
• Identify bottlenecks in queries, orchestration, and infrastructure.
• Implement observability (logs, metrics, alerts) for proactive issues.
• Ensure high availability, fault tolerance, and resiliency of pipelines.
• Collaborate with data architects, platform engineers, and stakeholders.
• Support financial reporting and regulatory data use cases.
• Enforce data engineering standards, security, and governance policies.

Skills

Airflow DAGs

dbt Core

Python

Kubernetes

OpenShift

SQL

Cloud platforms

CI/CD

Git workflows

Data engineering

Tools

Airflow

dbt Core

Kubernetes

OpenShift

Git

CI/CD tools

Docker

Job description

This is a 6-month contract position for a Senior Data Engineer based in Jersey City, New Jersey. You will focus on designing, building, and optimizing enterprise-scale data pipelines that support financial and accounting platforms, with particular emphasis on workflow orchestration, data modeling, and cloud-native infrastructure.

Responsibilities

• 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
• 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
• Deploy and manage data workloads on Kubernetes and/or 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
• 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
• 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

• Required
  • 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
  • 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
  • Experience running data platforms on cloud environments
  • Familiarity with containerized deployments, CI/CD pipelines, and Git-based workflows
• 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 such as Oracle