Data Analyst

📋 Description

• • Own the full data lifecycle for a portfolio of fast-moving AI and digital-transformation engagements, translating raw, messy client data into crystal-clear insights that drive revenue, reduce risk, and unlock new product opportunities.
• • Partner directly with cross-functional squads—strategy, engineering, design, and operations—to scope analytical questions, define success metrics, and ensure every recommendation is both statistically rigorous and immediately actionable.
• • Build and maintain scalable data pipelines in cloud environments (AWS, GCP, or Azure) using SQL, Python, dbt, and modern ETL/ELT tooling so that stakeholders can self-serve reliable, up-to-date dashboards without bottlenecks.
• • Design and iterate on Looker, Tableau, or Power BI dashboards that surface KPIs for C-suite executives, product managers, and client teams; craft narratives that turn complex findings into simple, compelling stories that inspire confident decisions.
• • Conduct exploratory analyses on customer behavior, marketing attribution, product usage, and operational efficiency to uncover hidden patterns, validate hypotheses, and prioritize the highest-impact experiments.
• • Drive A/B and multivariate test design, power calculations, and post-test analysis to ensure product and marketing experiments are statistically valid and ethically run, then socialize learnings across the organization.
• • Collaborate with data engineers and MLOps specialists to productionize predictive models—ranging from churn forecasting to recommendation engines—ensuring model performance is monitored, retrained, and continuously improved.
• • Champion data governance, privacy, and security best practices; document data dictionaries, lineage, and transformation logic so that knowledge is never siloed and compliance standards (GDPR, CCPA, SOC 2) are always met.
• • Translate client pain points into analytical roadmaps, estimate effort, and communicate trade-offs clearly so that leadership can allocate resources effectively and clients see tangible ROI within weeks, not quarters.
• • Serve as a trusted advisor during client workshops and sprint reviews, translating technical jargon into business language and empowering non-technical stakeholders to ask better questions and make data-informed decisions.
• • Contribute to Humanagency’s internal knowledge base by writing concise playbooks, reusable SQL snippets, and Python libraries that accelerate future engagements and raise the analytical bar for the entire team.
• • Embrace a culture of rapid iteration and constructive feedback—present findings early and often, invite critique from peers and clients, and iterate until the insight is bulletproof and the story is unforgettable.
• • Stay ahead of emerging analytics and AI tooling; run internal R&D spikes to evaluate new platforms (e.g., dbt Mesh, DuckDB, LangChain for structured data) and share concise recommendations on what to adopt, adapt, or skip.
• • Work fully remote with flexible hours (10–15 per week) across US and Canadian time zones, joining lively Slack stand-ups, virtual white-boarding sessions, and optional in-person off-sites designed to spark creativity and deepen relationships.