Analytics Engineer

Modal
New York +2 more
On-site

Who this role is best for

Analytics professionals with experience in data pipeline development and strategic consulting will find this role in AI infrastructure and self-serve analytics tools at Modal compelling.

Best fit for

  • Candidates with a background in building data tools and influencing product decisions through analytics
    — “Influence work on new products like LLM Inference Endpoints through product analytics tracking”
  • Professionals experienced in both SQL and Python with a track record in data pipeline creation
    — “Write data pipelines that power the operations of our business”

Things to consider

  • The role requires fluency in multiple data tools, including at least two from a specific list
    — “Professional experience with at least 2 of the following tools: Snowflake, dbt, dlt, Modal, Hex, Posthog”
  • Strong communication and relationship-building skills are critical for success
    — “Excellent and precise communicator and strong personability and relationship building skills”

How to stand out

  • Highlight experience in building foundational datasets for business decisions
    — “Create foundational datasets that can be used by people and AI tools to answer questions”
  • Emphasize past contributions to cost optimization in technical or financial contexts
    — “Identify millions of dollars of cost savings and optimization across our tools and financial operations”
Pace · Fast PacedCollaboration · MediumAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • successful development of data pipelines
  • implementation of self-serve ai analytics tools
  • identification of cost savings and optimizations
  • creation of foundational datasets
Typical background
professional experience with data tools like snowflake, dbt, dlt, modal, hex, posthoghigh attention to detailexcellent communication skills

Skills & requirements

Required

Sql FluencyPython ProficiencyData Pipeline DevelopmentData ReportingData AnalysisCloud ComputingData WarehousingData ModelingData VisualizationData Governance

Preferred

Ai ProductsLlm InferenceFin OpsFraud DetectionSales OpsRisk ManagementProject ManagementCommunity Engagement

Stack & domain

SQLPythonSnowflakedbtDltHexPosthogData PipelinesCloud Compute EconomicsSales CompFoundational DatasetsProduct Use CasesFinancial ReportingMarketing CampaignsAI AnalyticsKey Metrics TrackingExternal Customer ReportingProduct Analytics TrackingCost SavingsOptimizationFin OpsFraudSales OpsRiskToken Cost OptimizationCommunicationAttention To DetailPersonabilityRelationship BuildingAIInfrastructureDataAnalytics

About the role

Original posting from Modal via Ashby

ABOUT US:

AI needs a new infrastructure layer. We're building it at Modal.

Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.

Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.

We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.

Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.

ABOUT MODAL DATA:

We’re growing our Data team and are looking for our first few key hires to build self-serve data tools and drive business strategy in the right direction.

The mission of the Modal Data team is to make it easy to track company goals, make evidence-backed decisions, and prioritize the right work. We do this via:

  • Self-serve AI analytics tools (Hex, Snowflake)
  • Embedding with teams as a “data adviser”, providing strategic analysis and consulting

WHAT YOU'LL DO:

  • Contribute to building the most modern analytics stack https://modal.com/blog/analytics-stack in Data today to support AI-driven self-serve analysis, key metrics tracking, and external customer reporting
  • Influence work on new products like LLM Inference Endpoints https://modal.com/blog/qwen3-8-2-4t-a95b-now-available-on-modal through product analytics tracking
  • Identify millions of dollars of cost savings and optimization across our tools and financial operations
  • Write data pipelines that power the operations of our business, such as our cloud compute economics or sales comp
  • Create foundational datasets that can be used by people and AI tools to answer questions around product use cases, financial reporting, and marketing campaigns

WHAT YOU SHOULD HAVE:

  • SQL fluency, Python proficiency
  • Professional experience with at least 2 of the following tools: Snowflake, dbt, dlt, Modal, Hex, Posthog
  • Ability to extend their work beyond just data reporting and into action and impact
  • High attention to detail
  • Excellent and precise communicator
  • Strong personability and relationship building skills

NICE-TO-HAVE:

  • Experience with AI products, especially LLM inference and sandboxes
  • Experience in fin ops, fraud, sales ops, or risk, especially in the context of AI (e.g. token cost optimization)
  • Project management skills
  • Strong presence in the data community online and offline

Source: Modal careers (Ashby)

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