Senior Manager, Data Operations & Analytics (Product)

at  Lightspeed

Unlock the power of data at Lightspeed! As the Senior Manager, Data Operations & Analytics, you partner directly with the Product & Technology (P&T) team and lead your team to drive seamless data flows and optimize day-to-day data operations. You will be a key player of our Data Office, applying your data expertise and elevating data standards across the organization. If you’re ready to maximize the use of data to fuel product development and make a tangible impact, this is the opportunity you’ve been waiting for!

EXPERIENCE AND SKILLS:

• Extensive experience (8+ years) in a data operations, data management, or operations-related role, with at least 3 years in a leadership or mentoring position.
• Strong technical skills, including proficiency in data ingestion, transformation and modelling, analysis and visualisation tools (e.g. BigQuery, Dataform, Looker, SQL, GitHub, etc.)
• Experience with data governance, data quality management, and data privacy regulations (e.g., GDPR, CCPA).
• Excellent analytical, problem-solving, and project management skills.
• Strong business acumen and a passion for bridging the gap between technical insights and business strategy
• Excellent communication skills to articulate complex insights with clarity and impact, and strong interpersonal skills with the ability to collaborate effectively with cross-functional teams.
• Excellent organizational skills, self-starter mentality, and the ability to drive multiple priorities autonomously in a fast-paced setting

Leadership & Team Management

• Build and lead a team of DataOperations Analysts and Data Analysts working hand in hand with the different Product & Technology (P&T) teams inside of Pods. Each Pod will be responsible for managing its data pipelines, analysing feature adoption, productizing data, etc.
• Partner closely with the Product & Technology organization to develop and execute on a comprehensive Data Operations & Analytics strategy aligned with business, product and technology objectives.
• Oversee the development of the data roadmaps of your Pods, and help shape the broader Data Office roadmap to industrialise data and ensure robust data practices.
• Provide direction, mentorship and hands-on support to help your team members develop their skills and deliver value to the Product & Technology teams.
• Foster a culture of teamwork, collaboration, and continuous learning.
• Lead data-related projects, including migrations, implementations and upgrades.

Data Operations

• Develop a deep understanding of our products, systems, and underlying data architecture. Leverage this knowledge for you and the Data Operations Analysts to develop exploratory data models leveraging diverse data sources, supporting P&T teams with insightful datasets, reporting, and dynamic dashboards.
• Manage daily operations to collect, transform and store data.
• Monitor data quality and ensure data integrity across your Pods by fostering accountability.
• Drive a culture of continuous improvement within your Pod, identifying areas for automation and driving initiatives to enhance data processes, systems, and quality.
• Lead by example in embracing new technologies and methodologies to stay ahead of industry trends.

Analytics

• Lead the development and adoption of the highest analytics standards and methodologies among the Data Office and across the P&T organisation.
• Lead analysis and mining of large data sets to derive actionable insights using prescriptive, descriptive, predictive, exploratory, and inferential analysis.
• Support the creation of measurement plans using data for identifying trends, insights and answers to key business questions.
• Collaborate with cross-functional teams including data engineers, analysts, and business stakeholders to understand data requirements and help create user-friendly solutions.

Governance, Process and Quality Management

• Collaborate with the Data Office and your P&T stakeholders to establish and promote data standards and best practices. Ensure clear communication and alignment with key stakeholders to drive successful outcomes.
• Monitor data quality and ensure data integrity across your Pods by fostering accountability, investigating discrepancies at the source, documenting findings, and driving continuous improvement.
• Implement data governance policies and procedures.