Role Description Zenni is experiencing rapid growth in the affordable eyewear space, and data is at the core of our success. We are seeking a Manager, Data Engineering with a strong technical background and deep business domain expertise to partner closely with our Engagement Marketing team. In this leadership role, you will bridge the gap between complex data architecture and strategic marketing initiatives. You will be responsible for translating large datasets into clear, actionable insights, empowering our cross-functional teams to make confident, informed decisions. By building robust data solutions and fostering strong collaboration across departments, you will directly impact our operational efficiency and drive revenue growth. If you are a collaborative leader who excels at leveraging data to solve real-world business challenges, this could be the next move for you! This is a remote role overseeing a team of 2 and reporting to the Sr. Director of Data Engineering. Responsibilities - Champion data literacy: Provide ongoing training and support to the Engagement Marketing team, ensuring complex data concepts are seamlessly communicated and understood. - Drive cross-functional alignment: Act as the strategic bridge between the Data and Engagement Marketing teams, streamlining communication to boost operational efficiency and collaboration. - Power marketing platforms: Integrate enriched, highly trusted data with key business platforms (e.g., Google, Tik Tok, Meta) to optimize ad delivery and AI performance. - Ensure data integrity: Own the lifecycle of data feeds, encompassing validation, monitoring, testing, and proactive alerting, to guarantee uncompromising data quality and reliability. - Define success metrics: Partner with functional teams to establish a shared, accurate understanding of the core metrics that drive organizational performance. - Shape the data roadmap: Lead the strategic planning for the data team, ensuring technical initiatives directly align with Zenni’s overarching growth objectives. - Innovate continuously: Stay at the forefront of modern data engineering technologies and industry trends to evolve our strategy and keep our technical capabilities competitive. Qualifications - Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical discipline. - 8+ years of dedicated experience in the data area, coupled with at least 3 years navigating the fast-paced e-commerce landscape. - Deep business domain knowledge across key marketing functions, including but not limited to Performance Marketing, Lifecycle, and Brand marketing. - Hands-on experience implementing marketing measurement methodologies, including Last-Touch, MTA, incrementality testing, MMM, and CVR measurement frameworks. - Extensive experience designing data architecture, performing complex data modeling, and building scalable ETL/ELT pipelines for centralized data warehouses. - Advanced fluency in SQL and Python, with the ability to dive deeply into a modern tech stack as a hands-on contributor. - Demonstrated success leading projects from start to finish, spanning requirement gathering, cross-functional stakeholder communication, architecture design, implementation, and QA. - Proactive leadership and cross-functional communication skills, with a proven ability to drive business impact. Preferred Qualifications - Hands-on expertise working within Databricks environments. - Proven track record of guiding and mentoring high-velocity data teams in a fast-paced setting. - Demonstrated success managing complex data integrations, advanced reporting, and platform optimization across diverse marketing channels. - An enthusiastic, proactive approach to tackling new technical challenges and driving continuous improvement. Benefits - Annual bonus program - 100% employer-paid medical, dental, and vision (dependent coverage capped at $300/month) - Life, AD&D, and disability coverage (with optional add-ons) - Company-matched RRSP contributions up to 4% - Comprehensive wellbeing programs (mental, physical, emotional support) - Lifestyle Spending Account (wellbeing + learning) - Work-from-home stipend (for eligible roles) - Generous Flexible Time Off in addition to our Week of Zen - the time between Christmas and New Year’s to relax and recuperate - Fully Paid Parental Leave - 16 weeks for birthing and non-birthing parents - Family forming & fertility support and services through Maven - Annual Zenni Gift Card - to use towards our products Compensation In addition to other forms of compensation like perks and benefits, the estimated range for this role is CAD 163,000 - 185,000. The final offer will be determined based on permissible, non-discriminatory factors such as skill set, experience, geographical location, market conditions, and other organizational needs. The Company reserves the right to change, modify, or revisit the salary range for various reasons including business needs.

• Lead Data + ML Platform strategy and execution • Own technical direction for the data and ML platform, spanning data engineering, data platform, and ML Ops. • Invest in ML Ops capabilities that accelerate ML enablement: offline experimentation, model registry, shadow deployments and testing, drift/product monitoring, online and offline feature stores, and training data management. • Build measurement and experimentation frameworks for LLM and agentic workflows. • Invest in data platform and data engineering capabilities to handle large-scale streaming and batch data across modalities. • Modularize and scale our system, increase reliability, and modernize legacy infrastructure. • Make it easy for ML engineers to have ideas, test them, shadow deploy, gather data, test online, and ship. • Set clear success criteria for platform capabilities and hold the team accountable to outcomes. • Anticipate skill gaps and build a deliberate hiring plan; consistently attract and retain strong data and ML platform engineers. • Coach with depth: set explicit expectations, give direct feedback, and accelerate individual growth. • Build an environment of ownership, trust, and continuous learning. • Develop future leaders and create succession within the team. • Work closely with Product, Hardware, Data Science, and Analytics to shape problem definition and roadmap. • Translate ambiguous business needs into concrete technical direction. • Influence hardware and device design decisions where they intersect with data and ML needs. • Contribute to company strategy at the intersection of data, ML, devices, and care outcomes.

Role Description The Senior Big Data Engineer is responsible for designing, building, and delivering highly scalable big data and ETL solutions across distributed environments. This role supports projects at various and unanticipated client worksites throughout the United States, with headquarters based in Chicago, IL. The engineer works closely with cross-functional teams to develop, test, and deploy data solutions that meet business and operational needs, leveraging modern big data frameworks, cloud platforms, and agile development practices. Telecommuting is permitted. Job Responsibilities - Design and implement highly scalable ETL applications on Hadoop and Big Data ecosystems. - Develop new scripts, tools, and methodologies for streamlining and automating ETL workflows. - Deliver big data projects using Spark, Python, Scala, SQL, and Hive. - Design and create use-cases and scenarios for functional testing, integration testing, and system testing. - Work closely with Data Science, QA, Operations, and other teams to deliver on tight deadlines. - Participate in daily agile and scrum meetings and code reviews. - Coordinate with cross-functional operational teams for managing data delivery. - Write efficient, reusable, and well-documented code. - Prepare technical design documents for solutions. - Identify and address issues encountered from the data factory and provide timely solutions to incorrect or undesired results or behavior with ILD solutions. Technical Environment - Writing ETL Spark applications in PySpark and Scala, Flume. - Spark architecture, data frames, tuning Spark. - Relational databases (Oracle, PostgreSQL). - Python, SQL HQL, Hive. - Data Bricks. - Managing software systems using Hadoop, MapReduce, HDFS and all included services. - Distributed computing principles. - Big Data querying tools (Pig, Hive, Impala). - Data-warehousing and data-modeling techniques. - Core Java, Linux, SQL, scripting languages. - Cloud platforms (Azure). - Integration of data from multiple data sources. - Lambda Architecture. Qualifications - Bachelor’s degree in Computer Science, Information Systems, Computer Engineering, or related field plus 5 years of progressive experience as a Software Engineer/Developer or in Software Development required. - Experience writing ETL Spark applications in PySpark or Scala. - Experience with spark architecture, data frames, tuning spark. - Experience with relational databases (Oracle, PostgreSQL). - Experience with Python, SQL HQL, Hive. - Experience with Data Bricks. - Experience managing software systems using Hadoop, MapReduce, HDFS and all included services. - Experience with distributed computing principles. - Experience with Big Data querying tools (Hive). - Experience with data-warehousing & data-modeling techniques. - Experience with Core Java, Linux, SQL, scripting languages. - Experience with Cloud platforms (Azure). - Experience with integration of data from multiple data sources. - Experience with Lambda Architecture. - Telecommuting permitted. Circana Behaviors - Stay Curious: Being hungry to learn and grow, always asking the big questions. - Seek Clarity: Embracing complexity to create clarity and inspire action. - Own the Outcome: Being accountable for decisions and taking ownership of our choices. - Center on the Client: Relentlessly adding value for our customers. - Be a Challenger: Never complacent, always striving for continuous improvement. - Champion Inclusivity: Fostering trust in relationships engaging with empathy, respect, and integrity. - Commit to each other: Contributing to making Circana a great place to work for everyone. Location This position can be located in the following area(s): US Remote. Compensation The salary range for this role is 160,500 USD to 200,000 USD. This job is also eligible for bonus pay. Benefits - Comprehensive package of benefits including paid time off. - Medical/dental/vision insurance. - 401(k) to eligible employees. Application Process You can apply for this role through the Circana careers website or Intranet site for internal candidates.

The Role We’re looking for a product-minded Senior Data Engineer to lead the buildout of a new, graph-backed enterprise data platform at Fusion. This is not a maintenance role. You will architect and own a new data platform from the ground up—designing the ingestion layer, graph and relational storage, entity resolution pipelines, and APIs that unify resilience data across customers, systems, and cloud environments. You will define how data is ingested, resolved, modeled as a graph, governed, and exposed across Fusion’s ecosystem. This platform will power dependency analysis, recovery modeling, predictive intelligence, and a new generation of resilience products. This is a high-ownership opportunity for someone who wants to build something foundational, work with graph and network data structures at scale, and create a platform that becomes core to Fusion’s long-term strategy. Key Responsibilities • Architect and build Fusion’s next-generation data platform from the ground up, including a graph database layer, relational storage, and data lake components. • Design and implement scalable ETL/ELT pipelines to ingest and transform data from customer environments, internal systems, and third-party platforms using managed connector frameworks. • Build and maintain entity resolution pipelines that match, merge, and link records across disparate sources into a unified graph model. • Design and implement graph data models that represent operational dependencies, recovery sequences, and organizational relationships—supporting traversal queries across complex, multi-hop networks. • Develop temporal and bitemporal data models that capture how entities and relationships change over time, enabling historical replay and audit-grade versioning. • Establish best practices for data governance, quality, observability, lineage, and security across the platform. • Build backend services and APIs that expose graph queries, entity lookups, and data capabilities to downstream applications and ML systems. • Support containerized deployment across both managed cloud and customer-hosted (reverse SaaS) environments. • Partner with product and engineering leadership to shape the long-term data platform roadmap. Knowledge, Skills, and Abilities • Strong SQL expertise with experience designing performant data models and production-grade transformations. • Experience with graph databases or network-oriented data problems—e.g., dependency mapping, supply chain graphs, knowledge graphs, social network analysis, or similar domains where relationships between entities are central to the data model. • Familiarity with graph query languages or traversal patterns (e.g., Gremlin, Cypher, SPARQL, or recursive SQL) and an understanding of when graph representations outperform relational models. • Experience with entity resolution, record linkage, or deduplication at scale—whether using probabilistic matching frameworks, deterministic rules, or ML-assisted approaches. • Experience building data lakes, warehouses, and distributed data systems from the ground up. • Strong understanding of ETL/ELT patterns, orchestration (e.g., Airflow, Dagster, dbt, or similar), and pipeline reliability. • Experience with open-source or self-hosted data infrastructure components and a pragmatic sense for build-vs-buy trade-offs. • Experience designing and implementing enterprise system integrations, connectors, and APIs. • Strong engineering fundamentals with focus on scalability, performance, monitoring, and security. • Familiarity with containerized deployments and orchestration (Docker, Kubernetes, Helm, or similar) (bonus). • Experience with temporal or bitemporal data modeling patterns (bonus). • Experience with Salesforce or ServiceNow data models and integrations (bonus). • Strong Python or Java skills for building backend services (bonus). • Familiarity with AI-assisted development tools (e.g., Copilot, Cursor, Claude Code, or similar) and comfort using them to accelerate engineering workflows. • Product-oriented mindset with the ability to make pragmatic architectural decisions in ambiguous, early-stage environments Qualifications (Education and Experience) • Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field. • 5+ years of experience in data engineering, backend data systems, or platform engineering roles. • Experience building or significantly expanding a data platform or data infrastructure in a production environment. • Experience working with graph, network, or highly relational data structures in a professional or academic setting. • Experience working in cloud-native environments (Azure preferred). • Experience designing enterprise-grade integrations and connectors. • Experience with entity resolution or record-matching techniques (nice to have). • Experience with containerized deployments (Docker, Kubernetes) (nice to have). Milestones for the First Six Months In one month, you will: – Complete onboarding and gain deep familiarity with Fusion’s products, data strategy, and long-term platform vision – Assess the current state of data infrastructure and evaluate graph database and entity resolution options against platform requirements – Align with product and engineering leadership on platform scope and priorities In three months, you will: – Deliver the first foundational components of the new data platform—core graph storage layer, initial ingestion pipelines, and entity resolution workflow – Implement initial ETL/ELT workflows and at least one production-grade system connector – Establish standards for graph data modeling, governance, and observability In six months, you will: – Own and deliver the first production-ready version of Fusion’s new data platform, including graph traversal APIs and entity resolution – Have multiple ingestion pipelines and connectors operating reliably in production – Serve as the architectural owner of the platform, driving roadmap and technical direction – Propose and lead the next phase of platform expansion—temporal modeling, advanced graph analytics, and ML feature pipelines Compensation & Benefits The annual base salary range for this position is $135,000-$155,000, depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual bonus. Fusion offers a comprehensive benefits package including medical, dental, vision, and a 401(k) plan. Disclaimers Fusion is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, age, pregnancy, military service or discharge status, genetic information, sex, sexual orientation, gender identity, or national origin. Nothing in this job posting should be construed as an offer or guarantee of employment.