InsulaLabs

We are looking for a Senior Data Engineer. This is not a standard ETL role. You will work on a high-load system processing 10,000+ executions per minute, building scalable pipelines where AI and LLM logic are embedded directly into production data flows.

The role combines data engineering, backend thinking, infrastructure ownership, and real production AI integration.

What you will do:

• Design and maintain scalable data pipelines in Python on Google Cloud Platform
• Build and optimize AI-driven scoring flows using LLMs for real-time identity enrichment
• Work with BigQuery, Apache Spark, and Apache Iceberg for heavy data processing
• Maintain high-load orchestration with custom Python nodes and complex workflows
• Own deployment and release processes (CI/CD)
• Optimize PostgreSQL performance under heavy load
• Investigate incidents and resolve root causes permanently

• Use AI engineering tools such as Claude in daily development

   

Requirements:

• 4+ years of experience in Data Engineering / Backend Engineering
• Strong Python skills
• Commercial experience with production LLM integration (OpenAI / Anthropic / Gemini)
• Strong knowledge of GCP (Cloud Functions, IAM, Networking)
• Experience with BigQuery, Spark, Iceberg
• Good understanding of Kubernetes
• Experience with workflow orchestration tools (n8n is a strong plus)
• Strong practical knowledge of PostgreSQL

• English level B2+

   

What is important for us:

• Ownership mindset
• Fast learning ability
• Strong problem-solving approach
• Active AI usage in engineering work
• Focus on practical results over unnecessary complexity

We are looking for a Senior Data Engineer to join our team. This is not a standard ETL role. We are building a dynamic data ecosystem where AI is deeply integrated - both as a productivity multiplier and as a core component of our data processing logic for identity data enrichment and data scoring.

You will own the infrastructure that handles over 10,000 executions per minute, ensuring stability, scalability, and data integrity. You will work with a modern stack on Google Cloud Platform, utilizing Cloud Functions and Kubernetes.

We are looking for an engineer who improves infrastructure, automates everything, and is eager to implement LLM-based logic directly into high-load data flows.

Responsibilities

• AI-Driven Data Scoring: Design and implement pipelines that utilize LLMs to analyze and score identity data in real-time. You will integrate AI models directly into the decision-making loop, balancing accuracy with latency and cost.
• Own the Data Architecture: Architect scalable data solutions using GCP and Python. You will manage data storage and retrieval using BigQuery and Apache Iceberg to support querying of TBs of data.
• Heavy Data Processing: Utilize Apache Spark for data transformations and batch processing when lightweight cloud functions are not enough.
• Manage High-Load Orchestration: Maintain and optimize our system instances. It involves complex dataflows, custom Python nodes, and performance tuning for 10k+ execs/minute.
• Release Lifecycle (CI/CD): Take ownership of the deployment process, ensuring that updates to pipelines and infrastructure are released safely with proper testing and rollback strategies.
• Database Optimization: Manage PostgreSQL performance under heavy load, optimizing complex queries and indexing strategies.
• Active AI Usage: Use Claude Code and other AI engineering tools to accelerate your own development, refactoring, and testing processes.
• Incident Resolution: Proactively monitor the system. When alerts fire, you investigate the root cause—whether it’s a database lock or an LLM hallucination—and fix it permanently.
   

Requirements

• 4-5+ years of experience in Data Engineering or Backend Engineering with a strong data focus.
• Production AI Integration: Experience integrating LLMs (OpenAI, Anthropic, Gemini) into production applications via API. You understand latency, token limits, and how to structure data for AI scoring.
• Expertise in GCP: Understanding of Google Cloud Platform (Cloud Functions, IAM, Networking).
• Strong Python: You write clean, efficient, and testable code. You are comfortable building custom logic where standard tools fall short.
• Big Data Stack: Experience with BigQuery, Apache Spark, and modern table formats like Apache Iceberg.
• Kubernetes (K8s): Experience deploying and scaling services in containerized environments.
• Workflow Automation: Understanding of workflow orchestration tools at a deep technical level. N8N is a big part of our domain, so familiarity with it is highly valuable.
• PostgreSQL Mastery: Proven ability to handle heavy write/read loads and optimize schemas.
• English: B2+ (Upper-Intermediate) or higher.
   

Culture & Mindset

• Self-improvement: You are a fast learner. You don't fear AI replacing you; you master it to replace your manual tasks.
• Ownership: You treat the Identity domain as your own business. If a scoring model drifts or a pipeline slows down, you notice it and fix it without being asked.
• Internal Locus of Control: You take responsibility for outcomes. If an external API fails, you build a fallback mechanism instead of just blaming the provider.
• Get It Done: You prioritize shipping value. You know when to use a simple script and when to build a complex architecture.

Openness: You share knowledge freely. If you find a better way to prompt the AI for scoring, you share it with the team.

We are looking for a Senior Data Engineer. This is not a standard ETL role. You will work on a high-load system processing 10,000+ executions per minute, building scalable pipelines where AI and LLM logic are embedded directly into production data flows.

The role combines data engineering, backend thinking, infrastructure ownership, and real production AI integration.

What you will do:

• Design and maintain scalable data pipelines in Python on Google Cloud Platform
• Build and optimize AI-driven scoring flows using LLMs for real-time identity enrichment
• Work with BigQuery, Apache Spark, and Apache Iceberg for heavy data processing
• Maintain high-load orchestration with custom Python nodes and complex workflows
• Own deployment and release processes (CI/CD)
• Optimize PostgreSQL performance under heavy load
• Investigate incidents and resolve root causes permanently

• Use AI engineering tools such as Claude in daily development

   

Requirements:

• 4+ years of experience in Data Engineering / Backend Engineering
• Strong Python skills
• Commercial experience with production LLM integration (OpenAI / Anthropic / Gemini)
• Strong knowledge of GCP (Cloud Functions, IAM, Networking)
• Experience with BigQuery, Spark, Iceberg
• Good understanding of Kubernetes
• Experience with workflow orchestration tools (n8n is a strong plus)
• Strong practical knowledge of PostgreSQL

• English level B2+

   

What is important for us:

• Ownership mindset
• Fast learning ability
• Strong problem-solving approach
• Active AI usage in engineering work
• Focus on practical results over unnecessary complexity

The Go Backend Slot Game Engine Developer is the game-logic authority for the project. The role builds the server-side slot engine from scratch on a Go-based slot Game Development Kit (GDK) and owns it end-to-end — from the math model through integration
review, certification readiness, and live operation on the Remote Gaming Server (RGS) platform.
Scope of ownership covers the slot engine's game logic and mathematics (RTP structure, reel strips, paylines / clusters / Megaways / avalanche, win evaluation), the feature set (buy bonus, ante bet, free spins including retriggers and player-choice, max-win validation), the
paytable and reel/volatility configuration, the RTP simulation and validation harness, and the engine's conformance to the RGS integration contract — the engine lifecycle interfaces, the
spin/keep-generate/replay endpoints, RNG consumed exclusively through the SDK-injected RNG client, and SDK-compliant submission for platform integration review.
Technical Constraints
- Language: Go (1.24+).
- Transport: HTTP and gRPC
- Randomness: all RNG is delegated to the platform RNG service and consumed exclusively through the SDK-injected RNG client. Local or library randomness on game outcomes is treated as Non-Compliance.
- Configuration: reel sets, RTP and volatility externalized to configuration; simulator and runtime parameters via configurations and environment variables. No hardcoded math values in business logic.
- Version control: Git, with Go modules for dependency and version management.
- Local development: engine runs locally and connects to the development platform API via a tunnel; validated against SDK-provided mock services and simulators before submission.
- Deliverable: a compiled engine build submitted through the designated submission process, accompanied by build manifest (version, commit hash, build date), paytable configuration, and a signed SDK compliance statement.
Responsibilities
- Own the slot engine implementation: the engine lifecycle interfaces (spin generation, keep-generate for player-choice sequences, history replay / shown-state logic) and the conversion of game outcomes into the platform's wire model.
- Own the game mathematics: RTP structure, reel strips, paylines / clusters / Megaways, and win evaluation — modelled as the sole server-side authority on probability and outcome, with all probability and balance tables externalized to configuration.
- Own the feature set: buy bonus, ante bet, free spins (fixed, retriggerable, and player-choice), and max-win validation.
- Own the balance configuration tooling: a documented mechanism and config format so Game Design and math can update reel sets, RTP, volatility, and paytable values without code changes.
- Own the RTP simulation and validation harness: run the SDK simulator to prove target RTP, volatility, and max-win behavior, and attach the simulation report to each submission.
- Own SDK compliance and submission: use only documented SDK APIs, implement all required lifecycle methods, conform outgoing payloads to the published event and message schemas, stay within the supported SDK version range, and produce a signed compliance statement per build.
- Drive each build through the platform integration review: package the required submission artefacts, remediate documented review findings, and re-submit until integration is accepted.
- Author and maintain backend documentation: the math/game-logic specification, configuration format, architecture records, and a developer-readable README for the engine.
- Pair with the Game Designer and the client (frontend) team to translate design and GDD
intent into server-feasible, server-authoritative engine specifications.
- Own the engine's quality and security posture: unit-test coverage of game-logic modules, clean SAST/DAST scans, and no secrets in source, fixtures, or commit history.
- Actively use and document the use of AI tools (ChatGPT, Gemini, Claude) to accelerate scaffolding, math modelling, and documentation. Document outcomes alongside prompts so the workflow is reproducible by the team.
Requirements
The position has four hard pillars.
Pillar A — Go Service Architecture and SDK Integration
- Demonstrated ability to design a layered Go service in which the game-logic domain is unit-testable without booting the transport layer, the SDK, or any external service.
- Working knowledge of implementing engine lifecycle interfaces against a provided SDK:
spin generation, continuation (keep-generate) for multi-step sequences, and deterministic reconstruction of stored spins for history replay.
- Discipline in conforming to published event and message schemas, with consistent, well-defined error semantics across the contract.
- Versioning discipline: ability to evolve the engine and its wire payloads without breaking shipped client builds, including a documented deprecation policy, and to stay within the
SDK's supported version range without deprecated calls.
- Ability to externalize hand-tunable parameters (reel sets, RTP, volatility, paytable, limits) through configuration rather than code.
Pillar B — Slot Mathematics and Game Logic
- Demonstrated ability to model probabilistic slot mechanics where the server is the sole authority on probability, tier, and outcome.
- Probability, reel, and balance tables externalized to configuration. Hardcoded math or balance values inside business logic are treated as Non-Compliance.
- Currency and award math: integer or fixed-point arithmetic for all wager, balance, and award operations. Floating-point arithmetic on monetary values is treated as Non-Compliance.
- All randomness consumed exclusively through the SDK-injected RNG client; no local or library RNG on game outcomes.
- Working knowledge of common slot mechanics: paylines (left-to-right / right-to-left), clusters, Megaways, avalanche/cascades, free spins (fixed, retriggerable, player-choice), buy bonus, and ante bet, with max-win validation.
Pillar C — Determinism, Compliance, and Integrity
- Demonstrated ability to guarantee deterministic replay: a stored spin must reconstruct to an identical, player-visible state on history replay.
- Anti-abuse posture: idempotency for retry-prone operations, replay resistance.
- SDK compliance discipline: use of only documented APIs, no private or undocumented endpoints, correct lifecycle implementation, and a signed compliance statement per build.
- Secret-handling discipline: no secrets, signing keys, or credentials in source, fixtures, or commit history; awareness of and mitigation for common (OWASP Top 10) vulnerability categories.
Pillar D — Simulation, Performance, and Reliability
- Demonstrated ability to run large-scale spin simulation to validate statistical targets (RTP, volatility, hit rate, max-win) and to produce a reproducible simulation report.
- Working understanding of latency and throughput budgets for spin endpoints, including how serialization, allocation, and hot-path design affect them.
- Profiling expertise on a Go service: ability to localize latency or contention using application-level traces, profiling tools (e.g. pprof), and load-test evidence.
- Observability discipline: structured logs with correlation IDs across requests and metrics on the relevant endpoints.
- Testing discipline: unit-test coverage of game-logic modules with clean SAST/DAST scans.

We are looking for a Senior Data Analyst to join our global team. This is not a standard analyst role. We are building a dynamic identity resolution system where AI is deeply integrated — both as a productivity multiplier and as a core component of our data processing logic for identity resolution and LLM-based scoring.

We run enrichment against 70+ TB of data, apply AI and LLM-based scoring on top, and process around 20 million enrichment operations per day across hundreds of thousands of records.

The analytical challenges ahead are substantial: how to measure the quality of AI-driven scoring, how to validate that a change makes the system better, how to know where our money and time deliver the most value, and how to turn the questions our internal teams bring in into fast data-backed answers. You will own this side of the business, working daily alongside engineers and the product lead to turn these challenges into decisions that ship.

The main part of the role is not building dashboards — it is the data work that makes a meaningful dashboard possible in the first place.

 Concretely, you will be:

• Looking at data from multiple vendors side by side and deciding what to trust.
• Reconciling entities across sources that have different shapes and quality.
• Proposing and validating the logic that matches and scores them.
• Defining metrics and ground truth for questions nobody has asked before.
• Tracing why numbers don't line up across partial and sometimes contradictory sources.
  
  Responsibilities:
• Measurement & Evaluation Foundations: Define how we measure what matters — data quality, the accuracy of our AI scoring, how many records actually get matched to usable signals, cost per outcome. Build the feedback loops that tell us whether a change makes the system better or worse.

• Scoring Intelligence (co-owned with engineers): Partner with the engineering team on the LLM-based scoring system. You will build the evaluation framework for AI scoring, run experiments comparing prompts, models, and approaches against a baseline, and make data-backed calls on where we invest next. Our AI scoring system is actively evolving — you will shape how we measure and improve it.

• Client-related Analysis: Own the analytical side of the work that internal sales and media-buying teams bring in from pharma clients — how reachable a target audience is, how good the data looks, why a campaign performed the way it did. You turn their questions into data-backed answers they can use. Identity is a product core; the analyst works with internal teams, not with clients directly.

• Cost and Value Analysis: Turn cost and value into product decisions. Which data sources and enrichment scenarios pay off, where we overspend, what to cache, what to cut. We operate a mix of free owned data and paid external providers — the decisions have real dollar impact.

• Active AI Usage: Use Claude Code and modern AI engineering tools to multiply your own output. Automate the repetitive parts of analysis so you can focus on the decisions. We provide Claude Code licenses and expect active daily use.

• Data Quality Response: When pipeline output drifts, a provider degrades, or upstream data shifts, you notice first, diagnose the root cause, and drive the fix. You treat data quality as a product, not a ticket queue.
   

Requirements:

• 5+ years as a Data Analyst, Analytics Engineer, or Data Scientist in a product team (not consultancy, not BI-only roles).
• Advanced SQL at TB scale — BigQuery, Snowflake, or equivalent warehouse. You can read and write complex queries on tens of terabytes without someone pre-modelling the data for you.
• Python for analysis at a working level — pandas, Jupyter, or modern equivalents. Comfortable putting together a small script or helper when an existing tool doesn't cover your case.
• Built measurement or evaluation frameworks from scratch for systems that had no baseline. You are comfortable defining what and how to measure, not consuming someone else's KPI.
• Comfort with probabilistic systems. You understand that noise is inherent and 100% accuracy is not the goal — scoring, matching, ranking.
• AI-native workflow. You already use Claude Code, ChatGPT, Copilot, or similar AI tools daily and integrate them into your analytical process.
• English: B2+ (Upper-Intermediate) or higher.

Strong plus (one or more)

• LLM evaluation experience — A/B testing AI-powered features, output scoring, prompt iteration with measurable outcomes.
• Identity resolution / identity graph experience — adtech, martech, or customer data platform context (LiveIntent, LiveRamp, Epsilon, Experian Marketing, Acxiom, Merkle, or similar).
• Healthcare or pharma data — working with NPI, HCP-level data, healthcare taxonomies, pharma targeting.
   

Nice to have:

• Experience running controlled experiments on data pipelines.
• Prompt engineering or structured LLM prompt design.
• Prior work in AI-first product companies.

Culture & Mindset

• Self-improvement: You are a fast learner. You don't fear AI replacing you; you master it to replace your manual tasks.
• Ownership: You treat the Identity domain as your own business. If scoring quality drifts or a provider degrades, you notice it and drive the fix without being asked.
• Internal Locus of Control: You take responsibility for outcomes. If external data shifts or an API changes, you build around it instead of blaming the provider.
• Get It Done: You prioritize shipping answers. You know when to send a rough estimate and when to build a proper framework.

Openness: You share findings and admit mistakes openly — both wins and failures travel across the team. If you spot a better way to do something, you volunteer it without being asked.

We are looking for an Integration Engineer.

 In this role, you will design and scale data pipelines that process large volumes of advertising data, ensure stable data synchronization across external marketing platforms, and lead new integration initiatives from API evaluation to production rollout. You will play a key role in shaping the platform’s integration strategy and building scalable, AI-enabled integration solutions that power our core data infrastructure.

Requirements
- 5+ years in backend/data engineering, including 2+ years working with advertising/marketing APIs.
- Hands-on experience with Meta Marketing API and/or LinkedIn Marketing API: real work with rate limits, async reports, breakdowns, attribution windows.
- Strong Python skills: production-grade code with error handling, logging, and tests.
- Solid understanding of data pipeline patterns: idempotency, backfill, incremental sync, schema evolution.
- Experience with GCP or comparable cloud (AWS/Azure) at the service design level.

- Actively use AI-assisted development tools (e.g., Copilot, Cursor, Claude Code) in daily engineering workflows for code generation, debugging, documentation, and data pipeline design, applying sound engineering judgment when validating AI-generated outputs.

Nice to Have
- Knowledge of the Programmatic ecosystem: DV360, The Trade Desk, OpenRTB.
- Experience with BigQuery and/or ClickHouse.
- Pulumi / Terraform for IaC.
- Understanding of advertising metrics: impressions, clicks, conversions, ROAS, attribution models.

Responsibilities
- Own the architecture and evolution of all platform integrations: Meta Marketing API, LinkedIn Marketing API, Programmatic channels (DV360, The Trade Desk, etc.).
- Lead a team of 2-4 engineers: code reviews, sprint planning, mentorship.
- Design and maintain data pipelines: Ad Platform APIs to BigQuery/ClickHouse via Cloud Run Jobs and n8n workflows.
- Ensure data sync reliability: monitoring, alerting, error handling, retry strategies.
- Work with API rate limits, OAuth flows, and webhooks across advertising platforms - Drive new integration projects from API evaluation through production rollout. 
- Collaborate with the product team to prioritize the integration roadmap.

What We Offer

- Fully remote with flexible hours (aligned with EU timezones for syncs).
- Influence on the quality assurance strategy across the entire engineering department.
- A cross-team role with visibility into every part of the product.
- AI-first tooling.
- Claude Code licenses and cutting-edge AI development workflows. 
- A team with no bureaucracy, decisions are made fast.

Вимоги до володіння мовами

Англійська – Вище середнього

Українська - Носій мови

Data Engineer, Meta Marketing API, LinkedIn Marketing API, Python, Claude Code

Про компанію InsulaLabs

Insula Labs is a Ukrainian IT company.
We build skilled, talented teams that are committed to the long-term game. The main business model is outstaffing, which allows clients to quickly scale their teams thanks to access to our highly qualified specialists. 90% of our people are based in Ukraine. Work is organized in a remote format for everyone.
Our goal is to become a driving force for the successful projects of our partners, forming powerful teams that set the pace and achieve results. We strive for long-term cooperation in which each team member plays a key role and achieves professional growth.

About the Role

We are looking for a Senior Information Security Analyst to join our Information Security team. In this role, you will support security and compliance initiatives, participate in audits and risk assessments, help implement security frameworks, and contribute to improving the organization's overall security posture.

This position is ideal for someone with around 5 years of experience who wants to develop their expertise in information security, compliance, cloud security, and regulatory frameworks while working in an international environment.

Responsibilities

• Support information security and compliance initiatives.
• Participate in security audits and risk assessments.
• Assist with implementation and maintenance of security standards and policies.
• Support ISO 27001 compliance activities.
• Perform third-party risk assessments.
• Contribute to incident management and resilience testing.
• Collaborate with engineering and infrastructure teams on security best practices.
• Review cloud environments, access management, backup strategies, and change management processes.
• Work closely with a small Information Security team on project-based engagements.

Requirements

Experience

• Around 5 years of experience in Information Security, IT Audit, Compliance, or Risk Management.
• Experience participating in security audits and compliance activities.
• Understanding of information security principles and risk management.

Education

• Bachelor's or Master's degree in:
  • Information Security
  • Cybersecurity
  • Computer Science
  • Engineering
  • or a related field.

Certifications (preferred)

One or more of the following:

• CISA
• ISO 27001 Lead Implementer or Lead Auditor
• CISSP
• Cisco CCNA Security
• Other relevant Information Security certifications.

Technical Skills
Knowledge of cloud environments (AWS and/or Azure).

• Understanding of Kubernetes and Cloud Security concepts.
• Familiarity with:
  • Identity & Access Management
  • Backup & Recovery
  • Change Management
  • Security Governance
• Experience with Microsoft 365 / SharePoint and Google Workspace.
• Knowledge of SIEM platforms (e.g. Microsoft Sentinel) is a plus.
• Basic understanding of AI technologies is an advantage.

Industry Experience (preferred)

Experience in one or more of the following:

• Financial Services
• FinTech
• Banking
• Investment companies
• iGaming

Experience working with regulatory frameworks such as DORA would be highly valued.

Languages

• English — professional working proficiency (required).
• Ukrainian and/or Russian — preferred.

What We're Looking For

• Strong analytical and problem-solving skills.
• Ability to work independently and within a small team (1–4 people).
• Comfortable working in a back-office/support role.
• Willingness to work on project-based assignments (typically 2–6 months).
• Open-minded, collaborative, and able to work in diverse international teams.
• Successful completion of due diligence and conflict-of-interest checks before employment.

Nice to Have

• Experience with:
  • Third-party risk management
  • Incident response
  • Business resilience testing
  • Security governance
• Exposure to financial regulations such as DORA.
• Previous experience in iGaming or FinTech environments.

About the Role

We are looking for a Junior Information Security Analyst to join our Information Security team. In this role, you will support security and compliance initiatives, participate in audits and risk assessments, help implement security frameworks, and contribute to improving the organization's overall security posture.

This position is ideal for someone with around 2 years of experience who wants to develop their expertise in information security, compliance, cloud security, and regulatory frameworks while working in an international environment.

Responsibilities

• Support information security and compliance initiatives.
• Participate in security audits and risk assessments.
• Assist with implementation and maintenance of security standards and policies.
• Support ISO 27001 compliance activities.
• Perform third-party risk assessments.
• Contribute to incident management and resilience testing.
• Collaborate with engineering and infrastructure teams on security best practices.
• Review cloud environments, access management, backup strategies, and change management processes.
• Work closely with a small Information Security team on project-based engagements.

Requirements

Experience

• Around 2 years of experience in Information Security, IT Audit, Compliance, or Risk Management.
• Experience participating in security audits and compliance activities.
• Understanding of information security principles and risk management.

Education

• Bachelor's or Master's degree in:
  • Information Security
  • Cybersecurity
  • Computer Science
  • Engineering
  • or a related field.

Certifications (preferred)

One or more of the following:

• CISA
• ISO 27001 Lead Implementer or Lead Auditor
• CISSP
• Cisco CCNA Security
• Other relevant Information Security certifications.

Technical Skills
Knowledge of cloud environments (AWS and/or Azure).

• Understanding of Kubernetes and Cloud Security concepts.
• Familiarity with:
  • Identity & Access Management
  • Backup & Recovery
  • Change Management
  • Security Governance
• Experience with Microsoft 365 / SharePoint and Google Workspace.
• Knowledge of SIEM platforms (e.g. Microsoft Sentinel) is a plus.
• Basic understanding of AI technologies is an advantage.

Industry Experience (preferred)

Experience in one or more of the following:

• Financial Services
• FinTech
• Banking
• Investment companies
• iGaming

Experience working with regulatory frameworks such as DORA would be highly valued.

Languages

• English — professional working proficiency (required).
• Ukrainian and/or Russian — preferred.

What We're Looking For

• Strong analytical and problem-solving skills.
• Ability to work independently and within a small team (1–4 people).
• Comfortable working in a back-office/support role.
• Willingness to work on project-based assignments (typically 2–6 months).
• Open-minded, collaborative, and able to work in diverse international teams.
• Successful completion of due diligence and conflict-of-interest checks before employment.

Nice to Have

• Experience with:
  • Third-party risk management
  • Incident response
  • Business resilience testing
  • Security governance
• Exposure to financial regulations such as DORA.
• Previous experience in iGaming or FinTech environments.

We are looking for a Senior Data Analyst. 

This is not a standard analyst role. We are building a dynamic identity resolution system where AI is deeply integrated — both as a productivity multiplier and as a core component of our data processing logic for identity resolution and LLM-based scoring.

We run enrichment against 70+ TB of data, apply AI and LLM-based scoring on top, and process around 20 million enrichment operations per day across hundreds of thousands of records.

The analytical challenges ahead are substantial: how to measure the quality of AI-driven scoring, how to validate that a change makes the system better, how to know where our money and time deliver the most value, and how to turn the questions our internal teams bring in into fast data-backed answers. You will own this side of the business, working daily alongside engineers and the product lead to turn these challenges into decisions that ship.

The main part of the role is not building dashboards — it is the data work that makes a meaningful dashboard possible in the first place. Concretely, you will be:

• Looking at data from multiple vendors side by side and deciding what to trust.
• Reconciling entities across sources that have different shapes and quality.
• Proposing and validating the logic that matches and scores them.
• Defining metrics and ground truth for questions nobody has asked before.
• Tracing why numbers don't line up across partial and sometimes contradictory sources.

Dashboards and visualizations are part of how findings get communicated, but they come at the end — the surface of the work, not its core. Most of your time is spent on what sits underneath: methodology, judgment calls on data quality, and shaping how the domain understands its own numbers.

Responsibilities

• Measurement & Evaluation Foundations: Define how we measure what matters — data quality, the accuracy of our AI scoring, how many records actually get matched to usable signals, cost per outcome. Build the feedback loops that tell us whether a change makes the system better or worse.

• Scoring Intelligence (co-owned with engineers): Partner with the engineering team on the LLM-based scoring system. You will build the evaluation framework for AI scoring, run experiments comparing prompts, models, and approaches against a baseline, and make data-backed calls on where we invest next. Our AI scoring system is actively evolving — you will shape how we measure and improve it.

• Client-related Analysis: Own the analytical side of the work that internal sales and media-buying teams bring in from pharma clients — how reachable a target audience is, how good the data looks, why a campaign performed the way it did. You turn their questions into data-backed answers they can use. Identity is a product core; the analyst works with internal teams, not with clients directly.

• Cost and Value Analysis: Turn cost and value into product decisions. Which data sources and enrichment scenarios pay off, where we overspend, what to cache, what to cut. We operate a mix of free owned data and paid external providers — the decisions have real dollar impact.

• Active AI Usage: Use Claude Code and modern AI engineering tools to multiply your own output. Automate the repetitive parts of analysis so you can focus on the decisions. We provide Claude Code licenses and expect active daily use.

• Data Quality Response: When pipeline output drifts, a provider degrades, or upstream data shifts, you notice first, diagnose the root cause, and drive the fix. You treat data quality as a product, not a ticket queue.

Requirements

• 5+ years as a Data Analyst, Analytics Engineer, or Data Scientist in a product team (not consultancy, not BI-only roles).
• Advanced SQL at TB scale — BigQuery, Snowflake, or equivalent warehouse. You can read and write complex queries on tens of terabytes without someone pre-modelling the data for you.
• Python for analysis at a working level — pandas, Jupyter, or modern equivalents. Comfortable putting together a small script or helper when an existing tool doesn't cover your case.
• Built measurement or evaluation frameworks from scratch for systems that had no baseline. You are comfortable defining what and how to measure, not consuming someone else's KPI.
• Comfort with probabilistic systems. You understand that noise is inherent and 100% accuracy is not the goal — scoring, matching, ranking.
• AI-native workflow. You already use Claude Code, ChatGPT, Copilot, or similar AI tools daily and integrate them into your analytical process.
• English: B2+ (Upper-Intermediate) or higher.

Strong plus (one or more)

• LLM evaluation experience — A/B testing AI-powered features, output scoring, prompt iteration with measurable outcomes.
• Identity resolution / identity graph experience — adtech, martech, or customer data platform context (LiveIntent, LiveRamp, Epsilon, Experian Marketing, Acxiom, Merkle, or similar).
• Healthcare or pharma data — working with NPI, HCP-level data, healthcare taxonomies, pharma targeting.

Nice to have

• Experience running controlled experiments on data pipelines.
• Prompt engineering or structured LLM prompt design.
• Prior work in AI-first product companies.

Culture & Mindset

• Self-improvement: You are a fast learner. You don't fear AI replacing you; you master it to replace your manual tasks.
• Ownership: You treat the Identity domain as your own business. If scoring quality drifts or a provider degrades, you notice it and drive the fix without being asked.
• Internal Locus of Control: You take responsibility for outcomes. If external data shifts or an API changes, you build around it instead of blaming the provider.
• Get It Done: You prioritize shipping answers. You know when to send a rough estimate and when to build a proper framework.
• Openness: You share findings and admit mistakes openly — both wins and failures travel across the team. If you spot a better way to do something, you volunteer it without being asked.

What We Offer

• Fully remote with flexible hours, with working overlap in CET ±2 hours for syncs.
• AI-Native Environment: We provide licenses for Claude Code and encourage using the bleeding edge of AI tech — both for daily work and as part of product logic.
• High-Impact Role: You will directly shape how we identify, score, and reach healthcare professionals — the core business of our domain.
• Small team, direct impact: You will work directly with Product and Engineering leads — no layers between you and the people making decisions.
• No Bureaucracy: Fast decisions, no legacy processes, focus on results.

We are looking for an Integration Engineer to design, build, and maintain reliable integrations between our advertising platform and external marketing services.
 

What you will do

— Develop and maintain integrations with Meta Marketing API and LinkedIn Marketing API: rate limits, async reports, breakdowns, attribution windows.

— Build and support data synchronization pipelines from Ad Platform APIs to BigQuery/ClickHouse via Cloud Run Jobs and n8n workflows.

— Ensure reliability of data sync: error handling, retry strategies, monitoring and alerting.

— Work with API rate limits, OAuth flows, and webhooks across advertising platforms.

— Participate in new integration projects from API evaluation through production rollout.

— Write production-grade Python code: error handling, logging, tests, documentation.

— Actively use AI-assisted development tools (Copilot, Cursor, Claude Code) for code generation, debugging, and pipeline design.
 

Requirements

— 5+ years in backend or data engineering with focus on API integrations.

— Hands-on experience with Meta Marketing API and/or LinkedIn Marketing API: real work with rate limits, async reports, breakdowns, attribution windows.

— Strong Python skills: production-grade code with error handling, logging, and tests.

— Solid understanding of data pipeline patterns: idempotency, backfill, incremental sync, schema evolution.

— Experience with GCP or comparable cloud (AWS/Azure) at service level: Cloud Run, BigQuery, or equivalents.
 

Nice to Have

— Knowledge of Programmatic ecosystem: DV360, The Trade Desk, OpenRTB.

— Experience with BigQuery and/or ClickHouse.

— Pulumi / Terraform for IaC.

— Understanding of advertising metrics: impressions, clicks, conversions, ROAS, attribution models.

— Experience with n8n or similar workflow orchestration tools.
 

What We Offer

— Fully remote with flexible hours (aligned with EU timezones for syncs).

— AI-first tooling — Claude Code licenses and cutting-edge AI development workflows.

— A team with no bureaucracy, decisions are made fast.