Senior Engineering Manager, Horizon3

• Proficient in SQL. Able to read, write, and review data transforms and data quality checks in SQL
• Proficient in Python. Ability to review pipeline code and guide engineering decisions
• Competent in data analysis. Able to investigate anomalies, validate data quality issues, and find insight in data
• We welcome candidates from all backgrounds and experiences, and we encourage all qualified individuals to apply. Come be a part of Horizon3, where your unique contributions are recognized, and your potential is limitless

Desirable

• You will lead the team that provides the internal data platform that powers analytics and operational decision-making across Horizon3, to make high-quality, trustworthy data available to the business
• Drive and lead execution on a modernization of Horizon3’s data architecture
• Define data quality and timeliness standards. Drive data quality, observability and pipeline robustness efforts to provide reliable and performant access to data to consumers
• Act as a product owner to capture needs of product teams, BI teams, and other customers and manage the roadmap of data engineering initiatives
• Grow a team of data engineers, infrastructure engineers, and data analysts. Establish a culture of collaboration and engineering excellence
• Demonstrated expertise leading teams designing and operating cloud data warehouse in production (eg, Redshift, Snowflake, Databricks, BigQuery)
• Hands-on experience with the modern data stack: dbt for transformation, a pipeline orchestrator (Airflow, Dagster, or similar), and managed ingestion tooling (Fivetran, Airbyte, etc.)
• Experience implementing data quality frameworks and observability: defining pipeline SLAs, detecting and alerting on anomalies, and establishing tiered data sets with quality guarantees (e.g., medallion architecture)
• Able to partner with and influence peer engineering teams - drive alignment on shared standards such as pipeline patterns, data contracts, and quality guarantees across teams that own their own data sources
• Experience building or significantly growing a small data engineering team — including hiring, onboarding, and establishing engineering norms
• Proven ability to define and instill engineering culture: code review standards, definition of done, incident response, documentation practices, and culture of ownership
• Drives high-impact architecture decisions rigorously — requires design documents, runs structured reviews, builds consensus, and ensures decisions are well-reasoned before commitment
• Demonstrated experience acting as product owner for a platform or infrastructure team: maintaining a roadmap, triaging inbound requests, managing internal customer expectations, and making prioritization tradeoff decisions against capacity