Data Engineer Lead

Arine is hiring a Data Engineering Lead to lead a squad delivering and maintaining customer data pipelines. The role combines hands-on pipeline development with project delivery, data quality ownership, technical mentorship, and team leadership.

Responsibilities:

• Collaborate with the Implementation team and Data Analyst to review and own DE requirement writeups, ensuring they fully address data needs discussed in implementation calls
• Communicate timelines for deliverables to unblock Data Engineers for ETL pipeline development
• Proactively flag risks to timelines or quality to stakeholders and project managers
• Lead backlog grooming for implementation-related tickets
• Ensure team cross-coverage: both the DE Lead and assigned Data Engineer should understand the full ETL flow, with at least two additional engineers familiar at a high level
• Conduct thorough peer reviews and DE UATs after initial UAT completion
• Designing and maintaining data transformation pipelines using dbt, including utilizing core concepts like macros, incremental models and dbt tests
• Work with QA and CSA teams to resolve post-UAT issues and update the DE UAT checklist
• Provide technical guidance, mentorship to junior engineers, and promote best practices and coding standards
• Identify and escalate inefficiencies within and across teams
• Support project management and provide leadership to peers as needed
• Author and support high-quality technical documentation, assisting junior engineers in doing the same
• Collaborate with the DE Manager to report on DE contractor performance issues

Requirements:

• 6+ years working with data in production environments
• Proven ability to lead a small team (up to 3 engineers)
• Track record of building automated ETL workflows using Python and dbt SQL
• Hands-on proficiency with modern data technologies and comfort leveraging AI coding assistants to accelerate development, improve code quality, and enhance productivity
• Strong skills in data processing, validation, cleaning, and debugging across complex datasets
• Demonstrated success building production-grade dbt pipelines (macros, incremental and Python models, and testing)
• Working knowledge of agentic frameworks (e.g., LangChain, LangGraph, CrewAI, or the Claude Agent SDK) and hands-on experience building agents with tool use, memory, and multi-step reasoning, integrated with data platforms and pipelines
• Familiarity with agentic architectures and experience building, evaluating, and deploying AI agents (including MCP/tool integrations, guardrails, and observability) to automate data quality checks, pipeline monitoring, and analytics workflows
• Deep, demonstrable understanding of healthcare and healthcare claims data
• Comfort working with large-scale datasets (10GB+)
• Excellent verbal and written communication skills
• Must live in and be eligible to work in the United States
• Experience with AWS frameworks (i.e. S3, DynamoDB, Batch, Step Functions)
• Experience with Snowflake
• Skilled in data modeling for reporting and other business intelligence solutions