THE ROLE
🚀 We're growing! Our Data Services team is hiring and looking for a talented Senior Data Analyst who is ready to make an impact, grow their career, and work alongside an exceptional group of colleagues.
GOcxm is looking for an experienced Senior Data Analyst to turn GOcxm and client data into insights that drive decisions. This is a hands-on role. You'll cleanse and organize data, ensure every number is right, build predictive models, lead analytical studies, and tell clear, compelling stories with data for audiences ranging from brand teams to executives. You'll also partner with product and engineering to turn our best analyses into repeatable, productized capabilities within the GOcxm platform.
If you love digging for the insight no one has asked about yet, write Python as comfortably as SQL, and care as much about how data is governed as what it reveals, this role is built for you.
ABOUT GOcxm
GoCXM is a fast-growing technology company that helps consumer-facing brands deepen relationships with their customers and drive sales performance through a proprietary platform of integrated engagement, analytics, and field execution tools. The company positions itself as a unified growth platform for modern CPG brands.
We work with leading brands to turn marketing campaign and execution data into actionable insights that improve decision-making, strengthen accountability, and demonstrate measurable business impact.
WHAT YOU'LL DO
Data Quality, Cleansing & Preparation
- Cleanse, validate, and organize GOcxm and client datasets (survey, campaign, engagement, field execution, and sales data) into reliable, analysis-ready form.
- Write efficient, well-documented SQL and Python (pandas in Jupyter or similar notebooks) for extraction, transformation, and exploration.
- Own data quality: reconcile, validate, and triple-check every number before it reaches a client or executive.
- Write automated, repeatable data tests and validation checks (e.g., dbt tests, pytest, Great Expectations) so quality is enforced on every run, not checked once by hand.
- Identify data quality issues at the source and work with engineering to fix them upstream rather than patching them downstream.
Analytical Services & Studies
- Lead analytical services and studies for GOcxm and our clients: frame the question, design the approach, run the analysis, and deliver the recommendation.
- Apply sound analytical and statistical methods (segmentation, driver and importance analysis, trend and cohort analysis, test-versus-control measurement) to explain what happened and why.
- Build regression and predictive models (e.g., linear and logistic regression, tree-based models) to forecast outcomes, quantify drivers, and support client recommendations, and explain model results in plain language.
- Dig beyond the brief to surface new insights and opportunities for our clients and for our product.
- Deliver high-quality work in a fast-paced environment with tight client and product deadlines.
Reporting & Data Storytelling
- Produce user-friendly reports, graphs, and data stories that make complex findings clear and actionable.
- Build interactive data apps and prototypes in Streamlit, and dashboards using common BI tools (e.g., Looker Studio, Power BI, Tableau).
- Tailor insights to every level of the organization, from executive summaries for leadership to working sessions with analysts and account teams.
- Present findings directly to internal stakeholders and clients.
Productizing Analytics
- Partner with product and engineering teams to develop analytical procedures and turn repeatable analyses into documented, automated product capabilities.
- Contribute alongside the data engineering team on data warehouse design, including data models, dbt transformations, and metric definitions, so insights scale beyond one-off studies.
- Help define and maintain consistent KPIs and metric definitions across the platform.
Governance, Privacy & Responsible AI
- Handle GOcxm and client data with respect for governance, privacy, and contractual obligations, including applicable privacy regulations (e.g., SOC2, Quebec Law 25, GDPR).
- Apply appropriate access controls, anonymization, and aggregation practices based on the sensitivity of the data.
- Use AI tools responsibly to accelerate analysis and coding: generate code that is readable, reviewable, and tested; review and own every line before it ships; protect confidential data; and be transparent about how AI contributed to results.
WHAT WE'RE LOOKING FOR
Experience
- 7+ years of professional experience in data analytics, business intelligence, or applied data science.
- Proven track record of delivering analyses that changed business decisions, ideally in client-facing, consumer, marketing, or CPG/retail contexts.
- Demonstrated success working in a fast-paced environment with tight deadlines and shifting priorities.
Technical Skills
- Strong Python programming for data analysis (pandas, NumPy, Jupyter or similar notebooks); able to write clean, reusable, reviewable code. Required.
- Advanced SQL: complex joins, window functions, CTEs, and performance-aware querying. Required.
- Hands-on experience building regression and predictive models in Python (e.g., scikit-learn, statsmodels), including feature preparation, validation, and interpreting results.
- Experience writing automated data quality tests and validation checks for analytical pipelines and reports.
- Comfortable using AI coding assistants (e.g., Claude Code, GitHub Copilot) to generate clean, reviewable code, with the judgment to verify and test what they produce.
- Experience building interactive data apps with Streamlit or similar frameworks.
- Proficiency with at least one common BI tool (e.g., Looker / Looker Studio, Power BI, Tableau).
- Solid grounding in statistics and analytical methods, with the judgment to choose the right method and explain its limitations.
- Comfortable working in Git-based workflows: branching, pull requests, code review.
Communication & Mindset
- Excellent written and verbal communication skills; able to tell a clear, compelling story with data to technical and non-technical audiences.
- Meticulous attention to detail: you triple-check your numbers and would rather catch an error than explain one.
- Curious and self-directed: you dig for new insights rather than waiting for the question.
- Sound judgment with sensitive data and a practical, responsible approach to using AI.
- Collaborative; comfortable working across product, engineering, data engineering, and client teams.
Major Plus
- Knowledge of data warehouse concepts and dimensional modeling (star schemas, fact and dimension tables).
- Data engineering experience, especially with dbt (models, tests, documentation).
- Openness to contributing alongside the data engineering team on warehouse design and turning analyses into production data models.
Nice to Have
- Experience with Google BigQuery and the broader Google Cloud ecosystem.
- Experience analyzing consumer survey, market research, or campaign and promotion data.
- Familiarity with orchestration tools (e.g., Airflow, Dagster) or CI for analytics code.
- Background in CPG, retail, or marketing technology.
If you're passionate about innovation, collaboration, and delivering meaningful results, we'd love to hear from you. Apply directly or share this posting with someone in your network who may be a great fit.
