Lead DataOps Engineer (Only Visa Independent Candidate/W2)

Position Overview

Client is seeking a hands-on Lead DataOps Engineer to lead data engineering and operational initiatives within its Information Management group. This team supports enterprise data engineering, analytics, reporting, and ML engineering operations.

The lead will guide engineers, own technical delivery, and establish practices that make data pipelines and products reliable, automated, and ready for production. This role combines technical execution, team leadership, and collaboration with business, architecture, analytics, AI/ML, and platform teams.

Responsibilities

• Lead engineers in delivering data engineering, analytics, and DataOps initiatives.
• Design and implement DataOps frameworks, standards, and best practices.
• Partner with architecture, analytics, AI/ML, and platform teams to operationalize data products from development through production.
• Establish reliability expectations, data contracts, and change management practices for data products.
• Implement automation, pipeline orchestration, CI/CD, monitoring, and observability.
• Ensure solutions meet delivery timelines and technical and architectural standards.
• Translate business requirements into technical solutions and coordinate design patterns with architecture teams.
• Resolve technical issues, manage delivery risks, and escalate concerns as needed.
• Contribute to project prioritization, planning, and estimation.
• Conduct design, code, and test plan reviews.
• Coach and mentor engineers while improving team processes and delivery quality.

Required Qualifications

• 8+ years of experience in data engineering, analytics engineering, or platform engineering.
• Demonstrated experience leading engineers and taking ownership of complex deliverables.
• Strong communication and stakeholder collaboration skills.
• Strong experience with Azure, AWS, or Google Cloud Platform.
• Experience with Databricks or modern lakehouse architecture.
• Strong PySpark, SQL, data modeling, and modern data architecture skills.
• Experience building and orchestrating data pipelines using tools such as Azure Data Factory, Airflow, Dagster, or dbt.
• Experience with CI/CD tools such as Azure DevOps, GitHub Actions, or GitLab.
• Experience implementing DataOps or other automation frameworks.

Preferred Qualifications

• Azure ecosystem experience.
• Experience supporting AI or machine learning workloads in production.
• Familiarity with Event Hubs, Kafka, or Kinesis.
• Property and casualty insurance experience.
• SAFe or similar Agile delivery experience.
• Degree in Computer Science, Management Information Systems, or a related field.