Senior Data Analyst

TEEMA Solutions Group; Toronto; On-site; CAD 80,000 - 120,000; Full time

Job summary

A leading company in data enablement is seeking a Data Analyst to unlock analytics across the organization. This dynamic role involves developing data models, collaborating with product teams, and ensuring data governance. Candidates should have extensive experience in data management and visualization tools, with a strong understanding of the data lifecycle.

Qualifications

• 5+ years of experience in data management, data engineering, or analytics.
• Hands-on experience with data visualization tools.
• Strong understanding of data lifecycle management.

Responsibilities

• Develop scalable measurement strategies for products and services.
• Drive semantic data models for stakeholder insights.
• Ensure data privacy and governance throughout the data lifecycle.

Skills

Data Management

Data Engineering

Data Analytics

Product Management

Data Visualization

SQL

Data Lifecycle Management

Emotional Intelligence

Tools

Tableau

Looker

PowerBI

SQL

Snowflake

BigQuery

Databricks

Google Analytics

Adobe Analytics

Job description

Reporting to the Senior Manager, Data Enablement - this role will be responsible for unlocking analytics across the company. You will do this through a combination of data lifecycle ownership, data tooling enablement and outreach programs designed to foster the safe usage of scalable data models for reporting and analytic consumption.In this dynamic role, you will partner with product, engineering and program management to ensure that our product is emitting signals that enable us to measure product entitlement, adoption and utilization. You will triangulate product and customer needs with a variety of measurement options to create a set of semantic models that represent product usage for any audience. You will then internally promote the ongoing model development through creative and engaging communications to our stakeholders.An average week will find you planning measurement strategies with product leaders, reviewing engineering plans to capture critical data signals, developing data products that drive success metrics and creating documentation to ensure clarity, consistency and long-term usability. You will have multiple tools and teams at your disposal to achieve the core outcome of creating product and customer insights across the product unit

Description

• Partner with peers in product, engineering and program management to develop scalable measurement strategies for our products and services
• Drive scalable, semantic data models that can reach stakeholders both inside of company and across the enterprise
• Educate and enlighten data stakeholders with newly available objects and models that will unlock product and business insights
• Form deep knowledge of a single product domain while exerting influence over other product domains
• Build reusable data models that support key concepts of the business (Entitlement, Activation, Utilization, Incidents, Developers, Quality, etc.)
• Deliver executive dashboards and product insights based on foundational productionalized data models
• Partner with other analysts to create composite measures and insights that span the product unit
• Ensure that data privacy and governance is a first priority at all stages of the data lifecycle

REQUIREMENTS

• 5+ years of experience in data management, data engineering, data analytics, product management or a similar background
• 5+ years of hands on experience with data visualization tools (Tableau, Looker, PowerBI, etc)
• 2+ years of SQL experience
• Strong understanding of data lifecycle management including instrumentation, governance, modeling and self-service interfaces
• Build relationships with partners and stakeholders to understand product data needs, distill business requirements into technical documentation and prioritize work accordingly
• Excellent business acumen and emotional intelligence in ambiguous environments

And extra credit if you have experience in any of the following!

Third party web analytics tools (Heap, Google Analytics, Adobe Analytics, etc.)

Cloud based data warehouse technologies (Snowflake, BigQuery, Databricks, etc.)

Paywall, subscription or authentication based products

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