Remote Data Platform Engineering Manager

Summary

Join Gopuff's Data Platform team as a Data Platform Engineering Manager to lead the team's technical direction, shape data platforms and architecture, and drive continuous improvement.

Requirements

• 5+ years of experience in a data engineering or cloud/infrastructure engineering role
• 3+ years of engineering management experience
• Experience collaborating with functional stakeholders and establishing a roadmap that aligns with business priorities
• Experience with organizational design, team development, hiring, and performance management
• Experience building batch data pipelines using DAG-based tools such as Dagster or Airflow
• Experience deploying applications and service to Kubernetes and using related tools in the Kubernetes ecosystem (i.e. Helm, ArgoCD, Istio)
• Experience implementing DevOps best practices within the data platform, including solutions for CI/CD, data observability, monitoring, and lineage
• Experience in producing and consuming topics to/from Apache Kafka, AWS Kinesis, or Azure Event Hubs
• Experience with Infrastructure as code tools such as Terraform
• Experience developing real-time data pipelines using frameworks such as Apache Beam, Flink, Storm, Spark Streaming, etc
• Experience with data warehouses, data lakes, and their underlying infrastructure
• Proficiency in Python, SQL, RESTful API development
• Experience with cloud computing platforms such as Azure, AWS
• Experience with data governance, schema design, and schema evolution

Responsibilities

• Lead and mentor a team of Data Platform Engineers to develop Gopuff’s central data platform
• Drive continuous improvement within the team by developing practices around professional development, performance management, and hiring
• Provide clear technical direction and support for the team, ensuring the successful execution of data platform projects and initiatives
• Collaborate with engineering and product leadership to translate business requirements into effective technical solutions
• Develop and promote best practices for data collection, storage, and processing that impact the company-wide data strategy across Gopuff’s data lake and warehouse
• Architect and implement large-scale data platforms that enable analytics, data science, and machine learning in a multi-cloud environment
• Partner with software and analytics engineering teams to establish data contracts and improve data quality at every stage of the data lifecycle
• Manage project timelines, resource allocation, and team performance, ensuring that deliverables are met on schedule and within scope