Lead Data Engineer

Role Summary

Delivery and Stakeholder Leadership

• Own solutioning and engineering practice across multiple tracks, including scope, roadmaps, milestones, dependencies, RAID, and change control.
• Run governance cadences including weekly status, executive readouts, risk reviews, planning alignment, and steering updates.
• Act as the primary delivery interface for customer directors, technical managers, program and project managers, product teams, BSAs, engineers, and architects.
• Translate business priorities into actionable plans with acceptance criteria, sequencing, and release milestones.
• Communicate progress, tradeoffs, risks, mitigations, and decisions while managing escalations.

Technical Data Engineering Delivery

• Lead delivery across ingestion, streaming, transformation, orchestration, and data product enablement.
• Use Airflow for orchestration, scheduling, dependency management, and operational service-level agreements.
• Use Kafka for event and stream processing patterns and reliability considerations.
• Use Databricks or Spark for processing and pipelines, Snowflake for warehousing and analytics, and Postgres for operational sources and integrations.
• Drive technical governance through design reviews, coding standards, data modeling standards, performance practices, and reusable patterns.
• Ensure production readiness through monitoring, alerting, runbooks, incident response, root-cause analysis, and preventative actions.

Team and Operational Leadership

• Lead and motivate an approximately 20-person team, directly managing North American or onsite reports and influencing matrixed or offshore teams.
• Coach leads and scrum masters on execution discipline, stakeholder communication, and delivery predictability.
• Build a culture of ownership, accountability, collaboration, and continuous improvement.
• Drive automation and AI-assisted workflows for code review, test and data validation, documentation, runbooks, and incident triage.
• Implement Lean or Kaizen practices and operational metrics to reduce cycle time, defects, toil, and cost.

Required Qualifications

• At least 10 years of experience in technology, including at least 5 years in data engineering leadership for complex programs.
• Proven success delivering multi-track programs with complex dependencies and senior stakeholder engagement.
• Deep data engineering delivery experience in ETL or ELT, orchestration, data modeling, data quality, observability, monitoring, and production support.
• Advanced SQL and Python fundamentals are required.
• Experience with Snowflake and Databricks or Spark is required, with one platform able to be primary and the other requiring strong working knowledge.
• Knowledge of Airflow, Kafka, and Postgres concepts and operational patterns is required.
• Strong Agile delivery experience using Scrum or Kanban and tools such as Jira, Confluence, or equivalent is required.
• A bachelor’s degree in engineering or a related field is required.

Preferred Qualifications

• Retail domain experience in areas such as ecommerce, merchandising, inventory, supply chain, pricing, promotions, customer, or loyalty is preferred.
• Experience applying AI or automation to engineering delivery and operations is preferred.
• Data governance, PII handling, access controls, and audit readiness experience is preferred.
• Familiarity with Power BI, Tableau, semantic layers, or relevant professional certifications is preferred.