What are the responsibilities and job description for the Senior Data Engineer || Boston, MA || Fulltime || Onsite position at SmartIT Frame?
Role: Senior Data Engineer – Airflow, DBT Core, Kubernetes/OpenShift
Location: Boston, MA
Fulltime
Onsite
Job Summary
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.
Required Skills & Qualifications
· 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
· Cloud & Platform Experience
· Experience running data platforms on cloud environments
· Familiarity with containerized deployments, CI/CD pipelines, and Git-based workflows
Key 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
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)