data & AI
Diversity Report

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Inspiren – Senior Data Platform Engineer

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1. Role Overview

• Title: Senior Data Platform Engineer
• Department / Function: Engineering / Data Platform / Infrastructure
• Reports To (Name / Title): Likely Engineering Leadership / Head of Data / Platform Lead / Aaron (Hiring Manager)
• Level (IC / Manager / Director / Exec): Senior IC
• Reason for Hire (Growth / Backfill / Transformation): Growth + Platform Transformation
• Urgency / Timeline: High priority / active immediate search

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2. Compensation & Incentives

• Base Salary Range: $180,000 – $200,000
• Bonus (Structure / Target %): Likely discretionary / not primary lever
• Equity (Yes/No + Details): Yes, meaningful startup equity likely included
• Total Comp Range (if applicable): $200k+ depending on equity and experience
• Flexibility on Comp: Moderate to strong for top-tier talent

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3. Location & Work Environment

• Primary Location: Remote (US or Canada), NYC preferred
• Remote / Hybrid / Onsite (Days / Expectations): Fully remote with preference for proximity to NYC leadership hub
• Time Zone Requirements: EST or overlap strongly preferred
• Travel Requirements: Minimal occasional team offsites likely
• Relocation Offered (Yes/No): Likely case-by-case
• Visa Sponsorship (Yes/No): Unknown / likely limited

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4. Organization & Team Context

• Company Overview (Product, Mission, Market):
  Inspiren builds AI-powered technology for senior living communities. Their platform blends real-time monitoring, analytics, workflow intelligence, and operational tools to improve resident outcomes, caregiver efficiency, and profitability.
• Organizational Focus: AI, Data Infrastructure, Computer Vision, Healthcare Operations, Platform Scale
• Funding Stage / Revenue / Growth:
  Series B growth-stage company with ~$155M total funding and strong recent momentum.
• Team Size & Structure:
  Scaling engineering org with growing investments across data, ML, CV, and software engineering.
• Cross-Functional Partners:
  Engineering, Product, Analytics, ML, Data Science, Operations, Leadership
• Team Culture & Working Style:
  Fast-paced, mission-driven, high ownership, builder mentality, collaborative, pragmatic

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5. Role Purpose

• Core Problem This Role Solves:
  Build and scale the foundational data platform that powers analytics, ML systems, internal reporting, operational intelligence, and product decision-making.
• Why This Role Matters Now:
  Company growth is accelerating and data has become mission critical. Need mature infrastructure to support scale, reliability, and AI expansion.
• What Success Looks Like (6-12 months):

1. Streaming ingestion layer modernized
2. Databricks environment optimized for scale/cost
3. Governance and RBAC strengthened
4. Reusable tooling adopted by internal teams
5. Reliable platform trusted across business units

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6. Key Responsibilities

Core Ownership Areas

• Databricks platform architecture
• AWS data infrastructure
• Streaming ingestion (Kafka / Kinesis)
• Governance / RBAC / lineage
• Cost optimization
• Internal data tooling

Day-to-Day Responsibilities

• Build pipelines and platform components
• Optimize compute/storage spend
• Partner with ML / analytics stakeholders
• Troubleshoot reliability issues
• Improve observability and standards

Short-Term Projects (First 3-6 months)

• Assess current platform bottlenecks
• Improve ingestion reliability
• Implement quality frameworks
• Tighten governance model

Long-Term Initiatives

• Multi-year scalable data platform roadmap
• ML / AI data enablement
• Self-service analytics infrastructure
• Best-in-class internal developer experience

Stakeholder Interaction

Frequent interaction with engineering, product, analytics, ML, and leadership.

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7. Technical / Functional Requirements

Must-Haves

• Strong Databricks hands-on experience
• Modern lakehouse / warehouse expertise
• AWS cloud experience
• Pipeline architecture at scale
• Governance / RBAC understanding
• Strong Python / SQL
• Strong communication skills

Nice-to-Haves

• Kafka / Kinesis
• Startup experience
• Healthtech experience
• AI tooling adoption (Cursor / Claude Code)

Tech Stack

• Languages: Python, SQL
• Frameworks: Spark / PySpark
• Cloud / Infra: AWS, Databricks
• Tools / Platforms: Kafka, Kinesis, Airflow, Terraform, monitoring stack

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8. Ideal Candidate Profile

• Years of Experience: 5-10+ years
• Target Background:
  High-growth startups, healthcare tech, modern SaaS, data-heavy product companies, elite enterprise platform teams
• Education Preferences: Strong technical foundation; degree helpful not mandatory

Top Resume Signals

1. Databricks ownership
2. AWS production scale
3. Streaming ingestion systems
4. Governance / Unity Catalog / RBAC
5. Cost optimization + stakeholder impact

Key Traits / Soft Skills

• Ownership mentality
• Strong communicator
• Builder mindset
• Comfortable in ambiguity
• Pragmatic operator
• Cross-functional collaborator

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9. Red Flags / Non-Starters

• Pure BI / reporting profile
• No Databricks hands-on depth
• No production scale systems
• Weak communication
• Highly siloed enterprise-only mindset
• No ownership examples

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10. Interview Process

• Number of Stages: Likely 4-5
• Interview Format: Recruiter screen, HM screen, technical deep dive, stakeholder rounds, final
• Key Stakeholders Involved: Cameron, Aaron, Engineering leaders, cross-functional peers
• Timeline: Fast-moving if strong candidate identified

Assessment Areas

• Databricks depth
• Platform architecture
• Problem solving
• Communication
• Ownership / initiative
• Startup adaptability

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11. Hiring Criteria / Evaluation Framework

Core Competencies Being Assessed

• Technical depth
• System design
• Communication
• Leadership / ownership
• Business thinking
• Reliability mindset

Deal Breakers

• Cannot operate autonomously
• No hands-on architecture depth
• Weak stakeholder presence
• Overly theoretical profile

Nice Differentiators

• Healthtech experience
• AI/ML platform support experience
• Elite company pedigree
• Strong cost optimization track record

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12. Selling Points / Why a Candidate Would Join

• Mission-driven work improving elder care
• Real-world AI impact
• Strong funding and runway
• High ownership role
• Databricks + AWS modern stack
• Ability to shape platform strategy early
• Equity upside
• Meaningful technical challenges

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13. Additional Notes / Nuances

Need someone who can both build and influence. This should not be a passive ticket-taker. Strong preference for candidates who proactively create standards, tooling, and scalable systems.

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Data Engineer

Location: San Francisco OR New York City

This will be an onsite role, expectations are that you are in the office 3-4x per week

Compensation: Base $160,000 – $200,000 plus bonus

This role sits at the intersection of data engineering and investment analytics, responsible for building the data foundations that power AI systems, portfolio insights, and advanced analytics.

Key Responsibilities

• Build and maintain production-grade data pipelines powering AI systems, including ingestion from internal platforms, external vendors, and unstructured data sources
• Ensure data quality, consistency, and reliability across datasets used for AI applications and portfolio monitoring
• Collaborate with infrastructure teams on data architecture, integration, and security requirements
• Develop portfolio analytics solutions, including dashboards, external data integration platforms, and investment performance analysis
• Support analytics applications through engineering improvements and AI-assisted data processing workflows
• Provide quantitative and technical support to investment teams on live transactions using internal tools
• Build and manage external data vendor integrations, including APIs, schema documentation, and data lineage tracking

Requirements

• 2-4 years of experience in data engineering, building and maintaining production pipelines that feed GenAI systems
• Strong proficiency in SQL and Python
• Experience working with unstructured data is a must
• Experience with modern data stack tools (e.g. Snowflake, dbt, Airflow or equivalents)
• Experience with cloud data infrastructure (AWS), including ETL/ELT patterns and API integrations
• Exposure to datasets used in AI/ML workflows (e.g. embeddings, vector stores, retrieval pipelines) is a strong plus
• Familiarity with AI-native development tools and workflows (e.g. LLM-based coding assistants)
• Strong analytical mindset with attention to data quality and pipeline reliability
• Experience building dashboards and monitoring systems
• Strong communication and collaboration skills across technical and non-technical stakeholders
• Experience in financial datasets or financial services is beneficial but not required
• Bachelor’s degree in Computer Science, Engineering, Data Science, or a quantitative discipline

Please note: this role is not eligible for visa sponsorship and we are unable to accept C2C or contractor arrangements.

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Data Engineer

Location: San Francisco OR New York City

This will be an onsite role, expectations are that you are in the office 3-4x per week

Compensation: Base $160,000 – $200,000 plus bonus

This role sits at the intersection of data engineering and investment analytics, responsible for building the data foundations that power AI systems, portfolio insights, and advanced analytics.

Key Responsibilities

• Build and maintain production-grade data pipelines powering AI systems, including ingestion from internal platforms, external vendors, and unstructured data sources
• Ensure data quality, consistency, and reliability across datasets used for AI applications and portfolio monitoring
• Collaborate with infrastructure teams on data architecture, integration, and security requirements
• Develop portfolio analytics solutions, including dashboards, external data integration platforms, and investment performance analysis
• Support analytics applications through engineering improvements and AI-assisted data processing workflows
• Provide quantitative and technical support to investment teams on live transactions using internal tools
• Build and manage external data vendor integrations, including APIs, schema documentation, and data lineage tracking

Requirements

• 2-4 years of experience in data engineering, building and maintaining production pipelines that feed GenAI systems
• Strong proficiency in SQL and Python
• Experience working with unstructured data is a must
• Experience with modern data stack tools (e.g. Snowflake, dbt, Airflow or equivalents)
• Experience with cloud data infrastructure (AWS), including ETL/ELT patterns and API integrations
• Exposure to datasets used in AI/ML workflows (e.g. embeddings, vector stores, retrieval pipelines) is a strong plus
• Familiarity with AI-native development tools and workflows (e.g. LLM-based coding assistants)
• Strong analytical mindset with attention to data quality and pipeline reliability
• Experience building dashboards and monitoring systems
• Strong communication and collaboration skills across technical and non-technical stakeholders
• Experience in financial datasets or financial services is beneficial but not required
• Bachelor’s degree in Computer Science, Engineering, Data Science, or a quantitative discipline

Please note: this role is not eligible for visa sponsorship and we are unable to accept C2C or contractor arrangements.

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Data Scientist

London based role, two days per week on site with flexibility for candidates based further north. Salary up to £65,000.

This is an opportunity to join a well established data and analytics consultancy where data science sits at the heart of client delivery. You will work on commercially meaningful modelling projects, have regular exposure to end clients, and see your work directly influence real world decision making across large consumer datasets.

The Company

They are a specialist data and analytics consultancy that combines proprietary data assets with advanced analytics and modelling expertise. Operating as a solutions led partner, they support organisations across sectors such as utilities, telecommunications, financial services and the not for profit space. Their work focuses heavily on predictive modelling to improve marketing effectiveness, customer insight and engagement strategies at scale.

The Role

As a Data Scientist, you will be responsible for designing, building and refining models aligned to client and project needs, with a strong focus on propensity and scoring models. You will work closely with subject matter experts, project managers and data analysts, and in some cases take ownership of projects end to end.

Key responsibilities include:

• Building and training predictive models to solve real commercial problems, most commonly around customer behaviour and engagement.
• Leading data discovery sessions with client stakeholders to understand objectives, constraints and available data.
• Analysing large datasets to uncover patterns and translate findings into actionable insights.
• Presenting model outputs and recommendations back to clients in a clear, engaging way.
• Iterating on models to improve performance and ensure they remain fit for purpose.
• Contributing to internal or repeat client projects where you may work independently from end to end.

Your Skills and Experience

To succeed in this role, you will bring:

• Strong commercial experience using Python and SQL for data analysis and modelling.
• Hands on experience building predictive or propensity models within a business context.
• An analytical background with the ability to get involved in both modelling and broader analysis.
• Experience working with cloud based analytics platforms, with Databricks experience a strong advantage.
• Confidence working directly with clients, including presenting findings and explaining technical concepts to non technical audiences.
• The ability to balance multiple projects and adapt to different client environments and requirements.

What They Offer

• A salary between £50,000 and £65,000 depending on experience.
• A hybrid working model with two days per week in the London office and flexibility for candidates based outside the South East.
• Exposure to a wide range of industries and business problems through consultancy led work.
• Clear opportunities for development within a growing data science function, working alongside experienced data scientists and analysts.
• The chance to work on projects that have tangible commercial and customer impact.

How to Apply

If you are a Data Scientist looking to combine hands on modelling with client facing work in a collaborative consultancy environment, apply now to find out more.

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Data Scientist
London, hybrid two days per week | Salary up to £65,000

This role is ideal if you enjoy building predictive models that directly influence customer strategy and commercial decision making, while staying close to clients and real world use cases. You will work on varied projects, have autonomy over your models, and see your work used in live environments rather than staying theoretical.

The Company
They are a UK based data and analytics consultancy that combines large scale proprietary data with advanced analytics and modelling expertise. Working as a solutions led partner, they help organisations better understand, predict and influence customer behaviour. Their work spans multiple sectors, including utilities, telecoms, financial services and the not for profit space, delivering insight that drives measurable outcomes.

The Role
You will join a collaborative Data Science function working alongside Data Analysts, project managers and industry subject matter experts. Your responsibilities will include:

• Building, training and refining predictive and propensity models aligned to client objectives.
• Translating business problems into analytical solutions through data discovery and stakeholder engagement.
• Analysing model outputs and turning results into clear, actionable insight.
• Presenting findings and recommendations to clients in a clear and confident way.
• Owning end to end delivery on certain projects, particularly for internal or repeat client work.
• Contributing to model optimisation and continuous improvement over time.

Your Skills & Experience

• Strong commercial experience using Python and SQL for data analysis and modelling.
• Practical experience building predictive models, such as propensity or scoring models.
• An analytical mindset with the ability to contribute to both modelling and insight generation.
• Confidence working in client facing environments and explaining technical concepts to non technical audiences.
• Experience working with cloud based analytics platforms, with Databricks experience highly advantageous.

What They Offer

• Salary up to £65,000 depending on experience.
• Hybrid working with two days per week in the London office and flexibility for candidates based further north.
• Exposure to a wide range of sectors and use cases, keeping work varied and challenging.
• Clear opportunities to develop your technical capability and client facing skills within a growing data science team.
• A supportive, collaborative environment where your input and ideas are valued.

How to Apply
If you are a Data Scientist looking to combine hands on modelling with meaningful client impact, apply to learn more about the role.

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Senior Pricing Analyst

London, hybrid (2 days in office)

Salary – up to £85,000

This is an exciting chance to join a growing, mission-led ecommerce business where data sits at the centre of supply, logistics and operational decision making. You will shape how stock moves, how warehouses operate, and how the supply chain evolves as the business scales internationally. If you enjoy analytical freedom, hands-on problem solving and real commercial impact, this is an opportunity to step into a core role at a pivotal time.

The Company

They are a high‑growth, sustainability-focused ecommerce organisation with global operations and a strong data culture. With multiple warehouse locations, international offices and recent investment to accelerate transformation, they are expanding their analytics capability across supply and operations. The business combines technology, logistics and marketplace trading, offering you the chance to work at the intersection of supply chain performance, commercial optimisation and customer experience.

The Role

As Senior Supply Chain Analytics Lead, you will:

* Lead analytics across supply, inventory and operations, including stock levels, warehouse flows and fulfilment performance.

* Analyse channel behaviours and supply dynamics across direct, bulk and consignment models.

* Optimise warehouse capacity and operational efficiency through data-led insights.

* Conduct cost analysis across postage, shipping and fulfilment methods.

* Design, run and evaluate A/B tests and experiments across supply chain processes.

* Build analytical models and reporting using SQL, Python and Tableau or similar BI tools.

* Mentor at least one analyst and guide them through transformation-focused projects.

* Partner with senior stakeholders to shape operational strategy and support ongoing growth.

* Play a key role in scaling the analytics function as the business continues to evolve.

Your Skills and Experience

You will need:

* Strong commercial experience in supply chain, operations or inventory analytics.

* Hands-on capability in SQL, Python and BI tools such as Tableau or Power BI.

* Experience running experiments, designing A/B tests and analysing results with statistical rigour.

* Confidence working with large datasets and building clear, stakeholder-ready insights.

* Ability to communicate complex analysis in a simple, commercial way.

* A proactive, practical mindset with comfort operating in a fast-moving, changing environment.

* Experience mentoring or coaching others in analytics.

HOW TO APPLY:

Apply by sending your CV to Joe by the link below.

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Customer Insight Lead
£70,000-£90,000 + Bonus
Greater Manchester (Hybrid – 2x Days a Week in Office)

THE COMPANY

A leading UK health and life insurer, known for its innovative, rewards-led model that incentivises healthier lifestyles, is seeking a Customer Insight Lead. The business is backed by a global financial services group and continues to operate at scale; they’re investing heavily in data, analytics, and customer insight, and doubling down on personalisation and customer-centricity, with insight at the core.

THE ROLE

Reporting into the Director of Customer Insight, this is a senior individual contributor role acting as the strategic glue across Analytics, BI, and Insight.

• Define and drive the customer insight strategy and frameworks
• Bridge data, tech, and business teams to ensure insight drives decisions
• Partner with Marketing, Product, and Data Science
• Champion a customer-first mindset across the organisation
• Deliver hands-on analysis and insight to influence outcomes
• Support self-serve BI and scalable insight delivery
• Contribute to predictive and advanced analytics initiatives

Split: 50% strategy / 50% hands-on delivery

YOUR SKILLS & EXPERIENCE

• Strong SQL
• Python experience is strongly preferred
• Proven ability to turn data into clear, actionable insight
• Experience with predictive / advanced analytics or modelling frameworks
• Background in large, complex organisations (FS&I a plus)
• Strong stakeholder management with a focus on driving impact
• Comfortable operating across both strategy and delivery

THE BENEFITS

• £70,000 – £90,000

HOW TO APPLY

Please register your interest by sending your CV to Adam Osborne at Harnham via the Apply link on this page.

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