Head of Data Science & Analytics HybridFinance & AnalyticsFull time000-215 Toronto, Ontario, Canada Overview Description About Big Viking Games Big Viking Games is a Canadian gaming company focused on building, operating, and growing engaging online game experiences. Our teams work across product, technology, live operations, monetization, player experience, and content to create games and communities that last. We are entering a new phase of growth and modernization, with a focus on stronger data visibility, better decision-making, improved operational discipline, and practical adoption of AI across the business. This is a hybrid role with three (3) days in office. About the Role We are creating a new Head of Data Science & Analytics role to build and lead the data function across Big Viking Games. This is a hands-on leadership role for someone who can operate strategically, but also roll up their sleeves and personally build the analytics foundation. The right person will be comfortable working as a solo operator at first, owning the work directly before building out the team over time. This role will be responsible for transforming raw data into actionable insights that improve decision-making across product, live operations, monetization, marketing, finance, player experience, and executive leadership. We are also looking for someone who understands how modern AI can materially improve analytics, workflows, reporting, automation, and productivity. Experience implementing AI tools, agentic workflows, copilots, or automation systems will be highly valued. This is a newly-created role with significant opportunity to shape the data strategy, operating model, tooling, reporting structure, and future team. Responsibilities - Build and own the company’s data science and analytics function from the ground up. - Partner with executive leadership, product, live operations, marketing, finance, and technology teams to turn business questions into clear insights, recommendations, and action plans. - Develop the core analytics roadmap across player behavior, retention, engagement, monetization, game health, content performance, live operations, user segmentation, forecasting, and business performance. - Personally perform hands-on analysis, including SQL querying, dashboarding, data modeling, KPI development, reporting, statistical analysis, and business case development. - Improve the quality, consistency, and reliability of company metrics, reporting, and data definitions. - Identify gaps in data collection, instrumentation, data architecture, reporting, and operational visibility. - Partner with engineering and data stakeholders to improve data pipelines, data quality, data governance, and access to trusted datasets. - Design dashboards and executive reporting that help leaders understand what is happening, why it is happening, and what should be done next. - Lead analysis on player lifecycle, cohort behavior, monetization trends, feature performance, content performance, game economy, and live-service operations. - Support experimentation, A/B testing, measurement frameworks, and decision-ready analysis for product and business initiatives. - Implement practical AI-driven solutions to accelerate analytics workflows, automate repetitive reporting, improve insight generation, and support better business decisions. - Explore and deploy AI agents, copilots, workflow automations, and other tools that improve productivity across analytics, operations, product, and leadership reporting. - Establish best practices for responsible AI use, data privacy, analytical rigor, documentation, and repeatable workflows. - Over time, hire, coach, and lead a high-performing data science, analytics, and/or BI team. Requirements Qualifications - 8+ years of experience in data science, analytics, business intelligence, product analytics, or a related data discipline. - Experience operating in a gaming, digital product, SaaS, consumer technology, marketplace, or live-service environment. - Strong hands-on analytical skills, including advanced SQL and experience with Python, R, or similar analytical tools. - Proven ability to translate ambiguous business problems into structured analysis, clear recommendations, and executive-level narratives. - Experience building dashboards, reports, KPI frameworks, and decision-support tools for senior leadership. - Strong understanding of product analytics, user behavior, retention, engagement, monetization, segmentation, cohort analysis, forecasting, and experimentation. - Experience working with data engineering, product, finance, marketing, and executive teams. - Experience implementing or adopting AI tools, GenAI, copilots, agentic workflows, workflow automation, or AI-enabled analytics processes. - Ability to operate independently in a hands-on capacity before a larger team is built. - Strong communication skills, with the ability to explain complex analysis in clear business language. - Demonstrated ability to build structure in an environment where data, processes, tooling, or reporting may still be maturing. Nice to Haves - Experience with Snowflake or similar modern cloud data platforms. - Experience with gaming analytics, live-service games, virtual economies, content performance, player segmentation, in-game monetization, or player lifecycle analytics. - Experience with BI tools such as Looker, Tableau, Power BI, Sigma, Mode, Metabase, or similar platforms. - Experience with dbt, data modeling, data governance, experimentation platforms, or modern analytics engineering practices. - Experience building AI agents or automated workflows using tools such as ChatGPT, Claude, LangChain, Zapier, Make, n8n, Retool, or internal workflow automation tools. - Experience building a data function, hiring analysts, or scaling a small data team. Ideal Candidate Profile The ideal candidate is a builder, not just a manager. They have the seniority to set strategy and influence executives, but the humility and capability to do the work themselves. They are commercially minded, technically credible, and comfortable working in an environment where they may need to create structure from ambiguity. They understand that analytics is not just reporting. It is a decision-making function. They can identify what matters, build the systems to measure it, explain what the data means, and help the business act on it. They are also forward-looking in how they use AI. They should be able to bring practical, usable AI adoption into the company, not just talk about it conceptually. Benefits Compensation The expected compensation range for this role is based on experience, qualifications, and overall fit. Benefits - Comprehensive benefits package (health, dental, and vision) including HSA/WSA spending account from Day 1 - Participation in the Employee Stock Option Plan (ESOP) - RRSP participation and matching - 15 Vacation Days + 10 Wellness Days Big Viking Games is committed to creating an inclusive and accessible environment for all candidates. We welcome applications from individuals of all abilities and will provide accommodations throughout the hiring process as needed. If you require any accommodations, please email so we can work with you to support your needs.

• Own the annual operating plan (AOP) process - quota setting, capacity modeling, territory carve, and segment-level planning - translating strategic goals into a bottom-up GTM plan with clear assumptions. • Run the weekly forecast using commit/best/worst methodology, synthesizing Gong deal signals, pipeline movement, and coverage ratios into an accurate, defensible call of the number. • Build and maintain propensity-to-close, churn, and expansion models fed by product usage telemetry and firmographic data, surfacing risk and opportunity signals before they appear in lagging CRM metrics. • Architect and maintain the revenue data warehouse, owning identity resolution across product, marketing, CRM, and support data sources to create a unified GTM data layer. • Develop and continuously refine ideal-customer profiles scored on network complexity, device count, and change velocity; own TAM sizing and segment definitions. • Intentionally leverage AI to automate analytics, surface insights, and streamline GTM workflows - amplifying seller productivity and reducing time-to-insight across the revenue organization. • Partner with Sales, Marketing, Finance, and Customer Success leadership to build self-service reporting and ensure data-driven decision-making across the GTM organization. • Define, document, and maintain key revenue metrics and their calculation logic as the single source of truth for the business.

Role Description We're looking for a skilled professional to lead large-scale data migration initiatives, particularly in core banking and deposits. Qualifications - Bachelor’s or master’s degree in computer science, Information Technology, or a related field. Requirements - Total experience 8+ years. - 8+ years of experience delivering large-scale data migration initiatives (preferably core banking / deposits), including mapping, reconciliation, and cutover support. - Proven experience leading a data migration workstream and managing vendor delivery teams and cross-functional stakeholders. - Experience with legacy-to-modern platform migrations (AS/400 to modern core banking, cloud-native or microservices architectures) is strongly preferred. - Hands-on experience with DMS platforms or other core banking platforms like Flexcube, Temenos etc. - Data quality and validation concepts. - Working understanding of ETL concepts and migration lifecycle. - Understanding of banking data objects (customer/account, balances, product parameters, transaction history) and downstream impacts. - Strong analytical skills; comfortable working with large datasets and spotting patterns/variances. - Proficiency in SQL (preferred) and Excel; experience with data tools (e.g., Power BI) is a plus. - Strong documentation discipline (logs, trackers, evidence packs) and attention to detail. - Good communication skills to coordinate clarifications and follow-ups across IT, vendor, and business SMEs. - Structured problem-solving and ability to summarize issues and impacts clearly. Responsibilities - Define the overall migration strategy, scope, approach (phasing/co-existence if applicable), and acceptance criteria; align with program releases and cutover milestones. - Develop and maintain the data migration plan (timeline, environments, mock runs/dress rehearsals, final cutover), including dependencies with Integration, Testing, Operations, and legacy platform teams. - Govern vendor delivery: review and validate vendor migration design, runbooks, ETL approach, mapping specifications, transformation rules, and reconciliation logic; ensure clear RACI and delivery ownership. - Coordinate with banks business and IT SMEs to validate source-to-target mappings, reference data alignment, and critical business rules (e.g., product parameters, balances, interest, fees, account status). - Define and execute banks verification controls with the vendor (record counts, control totals, balance/GL tie-outs, exception management) and obtain stakeholder sign-off for each migration cycle. - Lead migration defect triage from a banks perspective: ensure issues are logged, prioritized, tracked to closure, and that RCA is completed for recurring defects; escalate risks early. - Ensure test data readiness for SIT/UAT and parallel run activities (data refreshes, data masking where required, refresh cadence, and data-related test support). - Plan and coordinate cutover data activities and readiness checkpoints (pre-cutover snapshots, load sequencing, reconciliation checkpoints, rollback/fallback approach) with minimal downtime and operational impact. - Ensure compliance with banks data governance, security, and regulatory requirements throughout migration (access control, secure transfer, audit trail, retention and masking for non-production). - Provide regular status reporting (progress, risks, issues, decisions) to program governance and senior stakeholders; maintain RAID and action logs for the data migration workstream.

Role Description We're looking for great new colleagues to join our team at Nagarro, a Digital Product Engineering company that is scaling in a big way! Requirements - Total experience 5.5+ years. - 5-8 years of experience in data analysis, system implementation support, testing support, or PMO/BA roles with strong data exposure (banking/financial services preferred). - Experience supporting data migration, reconciliation, or test data activities is an advantage. - Familiarity with core banking/deposits concepts (CASA/Time Deposit) is a plus. - Experience with legacy-to-modern platform migrations (AS/400 to modern core banking, cloud-native or microservices architectures) is strongly preferred. - Hands-on experience with DMS platforms or other core banking platforms. - Knowledge of data quality and validation concepts. - Working understanding of ETL concepts and migration lifecycle. - Understanding of banking data objects (customer/account, balances, product parameters, transaction history) and downstream impacts. - Strong analytical skills; comfortable working with large datasets and spotting patterns/variances. - Proficiency in SQL (preferred) and Excel; experience with data tools (e.g., Power BI) is a plus. - Strong documentation discipline (logs, trackers, evidence packs) and attention to detail. - Good communication skills to coordinate clarifications and follow-ups across IT, vendor, and business SMEs. - Structured problem-solving and ability to summarize issues and impacts clearly. Responsibilities - Maintain and update migration workplan trackers (mock run schedule, environment data refresh plan, reconciliation checkpoints, cutover readiness checklist). - Support requirements-to-data traceability: maintain source-to-target mapping tracker, transformation rule clarifications, and sign-off status across stakeholders. - Perform data validation activities using agreed control framework (record counts, control totals, sample-based checks, exception analysis) against vendor outputs. - Support reconciliation preparation and review (balance reconciliations, account-level variances, GL/SL tie-outs where applicable); document findings and follow up with owners. - Manage defect and exception logs for data migration (capture issues, categorize root cause themes, track to closure, support evidence collation). - Coordinate data-related SIT/UAT readiness: confirm test data availability, refresh requests, masking requirements (if applicable), and resolve data blockers with vendor/IT teams. - Prepare artifacts and evidence for governance and auditability (mapping approvals, run results, reconciliation reports, signoff packs, cutover runbooks attachments). - Produce regular status updates and dashboards for the Data Migration Lead (progress, risks/issues, decisions needed, upcoming milestones). - Support cutover activities and mock runs (checklist execution support, results collation, variance reporting, post-run lessons learned capture). - Ensure adherence to client data security and access controls when handling extracts, files, and non-production data. Qualifications - Bachelor’s or master’s degree in computer science, Information Technology, or a related field.