[Hiring] Data Engineering Manager @Cimpress/Vista

Role Description

Architect & Lead Operational Data Flows

• Design and oversee the implementation of an Operational Data Store (ODS) to provide a unified, real-time view of production data.
• Build low-latency data streams using technologies like Kafka or Flink to power embedded analytics directly within our customer-facing applications.
• Establish "Data Contracts" with upstream engineering teams to ensure high availability and schema stability for all real-time operational flows.

Evolve the Analytical Ecosystem

• Own the transition and scaling of our Analytical Data Store (e.g., Snowflake), ensuring it is optimized for both performance and cost-efficiency.
• Modernize our transformation layer by implementing robust ELT patterns and modular data modeling (using dbt and airflow) to support enterprise-wide reporting and building scalable data pipelines that power real-time decision-making across our global enterprise.
• Champion Data Governance, ensuring that every dashboard and report is backed by high-quality, audited, and well-documented data.

Enable Advanced Analytics & AI

• Build the "Data Foundation" for Machine Learning, including the development of Feature Stores and automated pipelines for model training and inference.
• Support AI initiatives by architecting solutions for unstructured data, such as integrating Vector Databases to power LLM-based features.
• Collaborate with Analytics teams to bridge the "MLOps" gap, ensuring that models move from notebooks to production seamlessly and reliably.

Strategic Leadership & Team Growth

• Mentor and grow a high-performing engineering team, fostering a culture of "DataOps" where automation, testing, and observability are the default.
• Manage the Data FinOps strategy, balancing the need for high-performance compute with long-term cloud budget sustainability.
• Act as a strategic partner to Product and Executive leadership, translating complex technical roadmaps into clear business value.

Qualifications

• Experience: 8+ years in Data Engineering, with at least 3+ years in a formal leadership or management role.
• Modern Data Stack (MDS): Proven experience architecting cloud data warehouses (Snowflake, BigQuery, or Databricks).
• The "Core Two": Expert-level proficiency in Python (for automation/pipelines) and SQL (for complex modeling and optimization).
• Cloud Engineering: Proficiency in AWS infrastructure management and event-driven pipelines (Kinesis, IAM, Monitoring, and IaC frameworks).

Requirements

• Real-Time Infrastructure: Hands-on experience with stream processing tools (Kafka, Flink, or Spark Streaming). You must understand how to move data in sub-second increments for your operational store.
• System Design: Ability to design ELT/ETL architectures from scratch using dbt, with a focus on idempotency, scalability, and error handling.
• Data Governance: Experience implementing data quality frameworks (e.g., Great Expectations, Monte Carlo) and ensuring compliance (GDPR/CCPA).

Nice to Have

• MLOps Knowledge: Familiarity with the ML lifecycle—specifically building Feature Stores and orchestrating pipelines via Airflow, Dagster, or Prefect.
• Vector Database Exposure: Experience with (or deep understanding of) Pinecone, Milvus, or Weaviate for supporting LLM/GenAI initiatives.
• Unstructured Data: Experience managing "Bronze" layer data lakes containing JSON, images, or text for AI training.
• FinOps Mastery: A track record of significantly reducing cloud warehouse costs through query optimization and compute management.
• Software Engineering Background: Previous experience as a Frontend or Backend Engineer.

Benefits

• Remote-First operating model and culture, with team members in over 15 countries and 30 US states.
• Autonomy and flexibility to work from home.
• Collaboration spaces for team members to work physically together.