Overview

About the Organization

Responsibilities

• Proactively surface strategic topics to senior leadership based on first-hand data engineering experience — platform constraints, technical debt, build-vs-buy trade-offs, and emerging AI/engineering tooling — rather than waiting to be asked.
• Define the data strategy and AI roadmap for a Databricks-oriented data engineering team, identifying the data products, lakehouse assets, and AI/ML capabilities that matter most over the next 12–24 months.
• Own the product roadmap for Databricks-native capabilities — lakehouse architecture, Unity Catalog governance, Delta Live Tables pipelines, MLflow/feature-store patterns— with hands-on proof points, not vendor-slide recommendations.
• Own sprint- and quarter-level backlog prioritization applying product-management rigor (RICE/WSJF-style scoring, stakeholder discovery, success metrics) to balance business value, technical debt, effort, and risk.
• Bring forward strategic topics — informed by direct engineering experience — that shape the roadmap rather than simply respond to it (e.g., platform limitations, emerging AI capabilities).
• Facilitate PI Planning, backlog grooming, and sprint reviews; serve as the voice of both the internal customer and the platform in delivery ceremonies.
• Own the data platform as a product — define its roadmap, SLAs/OLAs, and reliability targets, treating internal engineering teams and data consumers as its customers.
• Manage platform capacity, cost, and performance (compute/storage sizing, FinOps discipline, cost-per-workload tracking) to keep the platform scalable and cost-efficient.
• Standardize and rationalize the tooling landscape — reduce redundant components and own the platform's build-vs-buy and vendor evaluations.
• Own lifecycle management of platform components — versioning, deprecation, and migration planning — so platform evolution doesn't disrupt delivery teams.
• Define and implement the data KPI and metrics framework for the organization — data quality, pipeline reliability, freshness/latency, lineage coverage, and cost-to-serve — with clear owners and targets.
• Instrument dashboards and reporting (e.g., in Databricks AI/BI or equivalent) so KPIs are visible to engineering teams and business stakeholders, not buried in a backlog tool.
• Tie KPIs to business outcomes and review trends regularly with the team and stakeholders, using them to drive continuous improvement, not static reporting.
• Champion and enable AI adoption across data teams enterprise-wide, not only within owned pods — building the playbooks, training, and support that let other teams adopt AI-assisted engineering with confidence.
• Stand up a community of practice for AI-assisted and agentic engineering across data teams, sharing reusable patterns, guardrails, and lessons learned.
• Track engineering efficiency and AI-adoption metrics (cycle time, automation coverage, tool adoption rate) and prioritize backlog items that improve them.
• Prioritize data product delivery work that reduces time-to-data and time-to-insight for downstream consumers.
• Own backlog items that advance Data Capability Maturity — data contracts, self-service access, and reusable data products at scale.
• Redesign and own the data onboarding pipeline so new sources are cataloged, quality-checked, and available as trusted data products in days, not months.
• Streamline data access provisioning — self-service, role/attribute-based requests (e.g., via Unity Catalog), and automated approvals — to cut time-to-access.
• Track onboarding and access-acceleration metrics (time-to-onboard, time-to-access) and prioritize work that measurably shortens them.
• Directly manage a team of data engineers and/or product owners, including hiring, onboarding, and performance appraisals.
• Coach team members on product thinking and platform ownership, and serve as their career advisor, partnering with HR on development and IDPs.
• Partner with architecture, test engineering, and business/product leaders to align priorities and dependencies; represent the team in PI Planning.
• Communicate roadmap, platform trade-offs, and priorities clearly to both technical and business audiences.
• Implement and refine a standardized framework for backlog management, platform governance, and delivery execution (Agile/Kanban).
• Contribute to a growing community of practice among data product owners and platform leads; support Lilly's evolution toward a federated data mesh model.

Qualifications

• Quantitative background in engineering, computer science, data, mathematics, or a related field.
• 10+ years of relevant data/software engineering experience, including 4+ years of people-leadership experience and a demonstrated track record of product ownership.
• Deep, current, hands-on data engineering background — actively able to build, review, and troubleshoot pipelines and platform components, not solely credentialed from past roles.
• Demonstrated platform management experience: reliability/SLA ownership, capacity/cost management, tooling rationalization, and vendor/build-vs-buy decisions.
• Working product-management skillset: prioritization frameworks, stakeholder discovery, and defining/tracking adoption and outcome metrics.
• Strong working knowledge of data pipelines, data modeling, and cloud data platforms (AWS); hands-on experience with Databricks (Delta Lake, Unity Catalog, Delta Live Tables, MLflow) strongly preferred.
• Demonstrated experience defining data KPI/metrics frameworks and instrumenting dashboards that make quality, reliability, and cost visible to stakeholders.
• Demonstrated experience driving AI/ML-assisted engineering adoption beyond a single team — playbooks, training, or communities of practice that scale across data teams.
• Agile/Kanban methodologies with hands-on experience using Jira, Confluence, and ServiceNow (SNOW).
• Excellent analytical, communication, and executive-presence skills; proficiency in Python and/or SQL.
• Track record of shaping technology or platform strategy based on direct engineering experience, not just business requirements.
• Databricks certification (e.g., Data Engineer Professional) or equivalent depth of hands-on lakehouse experience.

Education

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field; advanced degree a plus.
• 10+ years of relevant experience in data/software engineering, delivery management, product ownership, or platform management, including 4+ years of people leadership.
• Demonstrated, current hands-on data engineering practice, alongside a history of contributing to or shaping technology/platform strategy based on that direct experience.