Description

This role will lead data engineering, DataOps, analytics, and data platform initiatives while partnering closely with architecture, analytics, AI/ML, and platform teams.

The ideal candidate brings strong technical expertise in modern data platforms, cloud technologies, data pipelines, and automation, along with proven experience leading teams and delivering complex data solutions.

Key Responsibilities

Lead a high-performing team in delivering complex data engineering and business initiatives.

Design and implement DataOps frameworks, standards, and best practices.

Partner with Data Architecture, Analytics, AI/ML, and Platform teams to operationalize data products end-to-end.

Establish standards for data reliability, data contracts, change management, and consistent data consumption.

Drive automation, orchestration, monitoring, and observability across data pipelines.

Ensure solutions meet delivery timelines, technical standards, and architectural requirements.

Collaborate with business and IT stakeholders to define requirements and develop scalable solutions.

Troubleshoot and resolve technical and project delivery challenges.

Identify and implement process improvements to enhance efficiency, quality, and reliability.

Partner with architecture teams on emerging technologies and modern design patterns.

Support project prioritization, planning, estimation, and delivery.

Coach and mentor team members while supporting technical talent development.

Ensure appropriate design, code, testing, and implementation reviews are completed.

Required Qualifications

8+ years of experience in data engineering, analytics engineering, platform engineering, or a related field.

Proven experience leading teams and taking ownership of complex technical deliverables.

Strong communication, collaboration, and leadership skills.

Hands-on experience with cloud data platforms such as Azure, AWS, or GCP.

Experience with Databricks and modern lakehouse architectures.

Strong knowledge of data pipelines and orchestration tools such as Airflow, Azure Data Factory (ADF), Dagster, or dbt.

Experience with CI/CD tools such as Azure DevOps, GitHub Actions, or GitLab.

Strong proficiency in PySpark and SQL.

Solid understanding of data modeling and modern data architecture.

Practical experience implementing DataOps, automation, and operational frameworks.

Preferred Qualifications

Experience with SAFe Agile or similar enterprise Agile methodologies.

Bachelor's degree in Computer Science, Management Information Systems, or a related field.

Familiarity with streaming technologies such as Kafka, Azure Event Hubs, or Kinesis.

Experience supporting AI/ML workloads in production.

Experience within the Property & Casualty Insurance industry.

Strong experience working within the Azure ecosystem

Key Skills

Education

Any Graduate