About the role
Applied AI Analytics Engineer
About the Role:
Applied AI Analytics Engineer represents an evolution of the traditional analytics role – moving beyond building models to bridging the gap between raw data and actionable, AI-driven insights. At NxtWave, you'll help shape what an AI-first analytics team looks like in practice.
You'll own end-to-end analytics, from extraction to insight, while actively pushing the boundary of what AI tools (Cursor, Hex, custom agents, MCPs, etc) can do for decentralized, high-speed decision-making. You'll partner closely with Product, Engagement, and Ops to turn EdTech-specific signals — learner behavior, engagement loops — into decisions.
We're looking for someone with a drive. A quick learner who adapts fast, picks up new tools on the go, and takes ownership of getting things done. You should have a strong analytical bent paired with the curiosity to bring AI-powered thinking into your everyday analytics work.
Roles and Responsibilities:
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Collaborate with cross-functional teams and translate business problems into data-driven strategies.
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Leverage Gen AI tools (Cursor, Hex, MCPs, custom agents, etc) to accelerate analysis, automate repetitive tasks, and build quick prototypes.
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Build modular dbt models and core bases on BigQuery that meet business needs – ensure versioning, quality monitoring and arch maintenance.
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Build self-serve interfaces that let stakeholders run their own analysis — Hex, custom agents, and MCPs — and drive their adoption across the team.
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Continuously explore AI-first engineering practices: architecture, efficiency, cost, and best practices for an AI-native stack.
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Stay in sync with stakeholders—understand the goals and decisions.
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Own data quality and sanity.
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Document best practices, maintain data dictionaries, and ensure standardization.
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Support forecasting models or ML-based prioritization efforts where relevant.
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Ensure timely, accurate, and high-quality data deliveries.
Skills Required:
Must-Have Skills (Core, Non-Negotiable)
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Advanced SQL on BigQuery and familiarity with analytics platforms (GCP/AWS/Azure) — building modular models, arch maintenance, query optimization, strong analytical bent.
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Demonstrated drive to learn and adopt new tools independently, especially in the AI / Gen AI space.
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Practical experience with Gen AI tools — Cursor, Hex, MCPs, and/or custom agents — to accelerate analysis and automate work.
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Hands-on with dbt — modular models, versioning, tests, and quality monitoring.
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Python — for automation, EDA, and data transformation.
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Exploratory data analysis (EDA) — profiling datasets, spotting anomalies, surfacing patterns.
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BI / visualization fluency — Looker Studio (or equivalent) and Google Sheets.
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Solid grasp of statistics and interpreting data distributions.
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Strong communication and stakeholder-management skills — translating business problems into data strategies and driving tool adoption.
Good-to-Have (Growth Path Skills)
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Experience with product analytics tools (Mixpanel, GA, etc.)
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Exposure to ML concepts
Work Experience:
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Minimum of1-2years of experience in the field of Analytics. A GCP-centric background (BigQuery, dbt) is preferred.
This is a ground-floor opportunity to define how an AI-first analytics team operates — not just using AI tools, but deciding which ones matter, building the infrastructure behind them, and bringing the rest of the organization along. If you want your analytics work to compound through automation and AI rather than repeat itself, this role is built for that.