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Were looking for a Senior Data Engineer in the Data Collection Ingest team to contribute to the design and coding of our data ingestion services & pipelines. This role involves working as part of a team that handles millions of requests per minute across multiple servers, and is responsible for a wide-range of data pipelines, processing billions of events each day.
Why is this role so important?
As the most trusted platform for measuring online behavior, millions of people rely on our insights daily as the ground truth for their knowledge of the digital world. Producing these insights requires large scale raw data to be ingested reliably in high scale to provide stable signals for analysis. As a Data Engineer you will have the opportunity to perform hands-on work and own our raw data ingestion pipeline end-to-end. Your work will have a direct impact on the quality and reliability of our data and the insights that our products are delivering to our customers.
So, what will you be doing all day?
Your role as Data Engineer of the Ingest Data Collection team means your daily responsibilities may include:
Design, code and manage end-to-end our data ingestion pipelines, both online and offline.
Take charge of developing & maintaining modern data infrastructure, while implementing best practices for building data pipelines.
Be responsible for high-scale ingestion services, solving challenges of availability, reliability, and scalability.
Run the production environment by monitoring availability and taking a holistic view of system health and data quality.
Own data infrastructure features from design to production using industry best practices with focus on quality and delivery.
Lead design & decision-making processes of the team.
Solve diverse complex problems of scale, performance and business logic.
Collaborate with product managers and other team leaders to plan, nurture, and implement an efficient and effective development process.
Continuously learn and evaluate new technologies in the everlasting effort to perfect our products
Perform code reviews, evaluate implementations, and provide feedback about potential improvements.
Improve your skills, learn from and mentor top-notch engineers and enrich other team members.
Have lots of fun!

Were looking for a Big Data Engineer to develop and integrate systems that retrieve, process and analyze data from around the digital world, generating customer-facing data. This role will report to our Team Manager, R&D.
Why is this role so important?
A data-focused company, and data is the heart of our business.
As a big data engineer developer, you will work at the very core of the company, designing and implementing complex high scale systems to retrieve and analyze data from millions of digital users.
Your role as a big data engineer will give you the opportunity to use the most cutting-edge technologies and best practices to solve complex technical problems while demonstrating technical leadership.
So, what will you be doing all day?
Your role as part of the R&D team means your daily responsibilities may include:
Design and implement complex high scale systems using a large variety of technologies.
You will work in a data research team alongside other data engineers, data scientists and data analysts. Together you will tackle complex data challenges and bring new solutions and algorithms to production.
Contribute and improve the existing infrastructure of code and data pipelines, constantly exploring new technologies and eliminating bottlenecks.
You will experiment with various technologies in the domain of Machine Learning and big data processing.
You will work on a monitoring infrastructure for our data pipelines to ensure smooth and reliable data ingestion and calculation.

We are looking for a Data Engineer to join our team and play a key role in designing, building, and maintaining scalable, cloud-based data pipelines. You will work with AWS services , Airflow and databricks to integrate, process, and analyze large datasets, ensuring data reliability and efficiency.
Your work will directly impact business intelligence, analytics, and data-driven decision-making across the company.
What Youll Do:
ETL & Data Processing: Develop and maintain ETL processes, integrating data from various sources (APIs, databases, external platforms) using Python, SQL, and cloud technologies.
Utilize LangGraph and other frameworks to create state of the art AI agents that integrate various tools and data sources.
Implement integrations that allow agents to take automated actions on behalf of users, streamlining workflows and reducing overhead.
Data Modeling: Design and maintain logical and physical data models to support business needs.
Optimization & Scalability: Improve process efficiency and optimize runtime performance to handle large-scale data workloads.
Collaboration: Work closely with BI analysts and business stakeholders to define data requirements and functional specifications.
Monitoring & Troubleshooting: Ensure data integrity and reliability by proactively monitoring pipelines and resolving issues.

Required Data Scientist Team Manager
A day-to-day role as a Team Manager includes:
Managing a multidisciplinary team of data engineers, data scientists, and data analysts.
Leading the development of advanced and creative solutions for complex technical challenges in areas like big data and machine learning.
Collaborating closely with data extraction, platform, and product teams.
Communicating with business stakeholders and gathering feedback from customers.
Hiring new team members and developing the groups structure.
Playing a crucial role in shaping product strategy within a startup-like environment.
Day-to-day, the team you will lead will:
Develop statistical and machine learning algorithms on large-scale data to estimate metrics, create features, and deliver insights.
Own end-to-end data processes and pipelines, from research and proof of concept to production.
Collaborate with product and R&D teams to plan and deliver best-in-class digital insights.
Design, validate, and monitor ongoing processes to ensure quality and reliability.
Identify, analyze, and monitor new raw data sources as needed.

Were looking for a Data Engineer with a strong applied ML focus to join our R&D department!
Why is this role so important?
Our Retail Intelligence products help leading brands and retailers understand how their products, brands and categories perform online. Behind them is data collected from retailers and marketplaces around the world: product pages, brands and categories, each described differently by every site.
Your mission is to turn that data into a single, trusted view: classifying products into a unified taxonomy, normalizing brands and attributes, and matching the same entities across sources. And doing it at scale, across a catalog of more than a billion records that keeps growing and changing every day.
This is an applied ML role within data engineering. Youll build with LLMs, agentic frameworks such as LangGraph, embeddings and classical ML, and ship them as production pipelines. Its hands-on work, not research for its own sake, but it takes a real understanding of classification and NLP methods to choose the right tool for each problem and prove that it works.
So, what will you be doing all day?
Your daily responsibilities may include:
Designing and building LLM-powered and ML-based pipelines that classify, normalize, structure and match product, brand and category data
Building agentic workflows (LangGraph or similar) that automate complex data tasks end to end
Choosing the right approach for each problem (LLMs, embeddings, fine-tuned models, classical classifiers or rules), balancing accuracy, cost and latency
Scaling solutions to run efficiently over billions of records, using Spark, Databricks and our cloud infrastructure
Building evaluation frameworks: ground-truth datasets, labeling processes, quality metrics and ongoing monitoring
Taking solutions from POC to production, and owning them after launch
Working closely with Product to define requirements and shape the roadmap
Collaborating with data engineers, data scientists and other R&D teams on infrastructure and best practices.

A growing B2B SaaS company is looking for an experienced Head of Data Science to lead its Data Science function and drive the next generation of AI and machine learning capabilities.
This is a strategic leadership role combining people management, hands-on problem-solving, cross-functional collaboration, and ownership of data-driven innovation across the organization.
Responsibilities
Lead and mentor a global team of Data Scientists
Define and execute the company's Data Science strategy
Drive the development of advanced machine learning, optimization, and forecasting solutions
Partner closely with Product, Engineering, Customer Success, and executive leadership
Translate complex business challenges into scalable analytical solutions
Support customer-facing initiatives and strategic engagements when needed
Establish best practices for experimentation, model evaluation, and performance measurement
Act as a key stakeholder in shaping the company's AI vision and roadmap.

We're looking for a Data Engineer to own our data architecture end-to-end. You'll design, build, and scale the pipelines that power every pricing decision we make - from raw ingestion through our data lake to the analytics and BI layers our customers rely on. This is a hands-on leadership role: you'll set technical direction, drive architectural decisions, and mentor a team of data engineers, while still writing code and owning delivery.
What You'll Do:
Architect and build large-scale batch and streaming ETL/ELT pipelines
Own data lake design, table modeling, and partitioning strategy across billion-row datasets
Drive query performance and cloud cost optimization across AWS and GCP
Establish data quality, observability, and reliability standards - freshness, correctness, and SLAs
Partner with backend, product, and data science teams to expose data through internal and customer-facing APIs
Our Stack
Spark / EMR Airflow BigQuery PostgreSQL & Aurora Kafka RabbitMQ Elasticsearch Go Python AWS GCP Kubernetes Terraform.

As our Founding Data Engineer, you will be the first dedicated Data Engineer at the company, with the opportunity to build our data engineering function from the ground up. You'll own the architecture and foundations of our internal data platform - the central warehouse, pipelines, and models every team relies on to understand how our product is performing, and provide tools for day to day operations. You'll define how data from across the company is collected, modeled, transformed, and governed, and establish the standards and best practices that will scale with us as we grow. Working closely with Engineering, Data Science, Delivery, Product, and Analytics teams, you'll turn data into reliable, structured assets that power product and business decisions.

What Youll Do
Build our Data Engineering Function: Be the first dedicated Data Engineer and will be required to be opinionated about the warehouse architecture, tooling, standards, and best practices for how we work with data across the company.
Develop Data Pipelines: Design, build, and maintain data pipelines that transform company data into structured, queryable assets.
Enable Data-Driven Decisions: Partner with Engineering, Data Science, Product and Analytics teams to deliver trusted data foundations and actionable insights.
Create BI Dashboards: Build and maintain dashboards and reporting layers that support product, and operational decision-making.
Ensure Data Quality and Accessibility: Implement best practices for data modeling, governance, monitoring, and reliability.

Were looking for a Senior Data Scientist to be a core driver in how our product empowers security teams. You will be expected to deeply understand customer needs and translate them directly into product features that deliver real value. You'll own key parts of our frontend stack, drive key architectural decisions, and turn complex security data into clear, actionable business insights.

In this role, you will design, build, and productionize advanced models that help organizations secure AI systems across data, models, and runtime usage. Youll work closely with Product, Engineering, and Security teams to turn complex, real-world security problems into scalable, high-impact AI solutions.

This is a high-impact, hands-on role with true ownership, where your work directly shapes the intelligence powering a category-defining AI Security platform.


What Youll Do:

Own End-to-End Model Development (Research → Production)

Take full ownership of the model lifecycle: problem definition, research, prototyping, training, evaluation, deployment, and ongoing optimization.

Design and build scalable ML / AI solutions to detect, analyze, and mitigate AI-related security risks.

Ensure models are robust, explainable, and production-ready in real-world enterprise environments.

Partner Deeply with Product & Engineering

Collaborate closely with Product Managers to translate customer pain points and security requirements into clear modeling objectives.

Work hands-on with engineering to integrate models into production systems and data pipelines.

Balance research depth with pragmatic execution and business impact.

Build AI Security Intelligence

Develop capabilities across areas such as AI behavior analysis, anomaly detection, model governance, data risk detection, and usage observability.

Work with structured and unstructured data, including logs, prompts, model outputs, and metadata.

Continuously iterate on models based on usage patterns, feedback, and emerging threat vectors.

Lead Research POCs & Innovation

Stay at the forefront of LLMs, applied ML, and AI security techniques and bring new ideas into the product.

Were looking for a talented Data Engineer to join our AI team and build the data foundations that power our AI systems at scale. As a key member of the AI team, youll be responsible for designing, building, and operating the data layer that enables our AI models and agents to perform in production. This includes data ingestion, storage, transformation, monitoring, and defining the data strategy that ensures our AI applications receive high-quality, reliable, and timely data.

Were seeking an ambitious Data Engineer who is passionate about building robust data infrastructure, working with large-scale and complex datasets, and partnering closely with AI engineers and researchers to translate model needs into production-grade data systems.


WHAT YOU WILL DO
Design, build, and operate scalable data pipelines (batch + streaming) that power our AI systems in production.
Own the end-to-end data lifecycle: ingestion, storage, transformation, serving, and continuous improvement.
Build and maintain the data layer from raw data to semantically accessible data that enables AI models and agents to perform reliably at scale.
Ensure high data quality and high-standard operation through monitoring, alerting, and validation checks.
Work with modern storage systems (data lakes, warehouses, relational, graph, and vector databases) to support diverse AI workloads.
Partner closely with AI engineers and researchers to translate model requirements into production-grade data infrastructure.
Define and evolve the data strategy to for AI applications.

Were looking for a Data Analyst who combines analytical rigor, strong business judgment, and the ability to turn complex data into clear, actionable insights.
This role spans three core areas: marketing analytics for our clients, product analytics for our company, and modeling consumer behavior in AI chats using panel data.
Youll help clients understand marketing effectiveness through attribution, marketing mix modeling, and incrementally analysis. Internally, youll analyze product clickstream data to understand how customers use our company and identify opportunities to improve adoption, engagement, and retention. Alongside this, youll analyze panel and conversational data to understand how people discover brands, explore options, and progress through decision-making journeys in AI chats.
Youll own analytical projects end to end from defining the question and preparing the data to developing analytical models, validating findings, and influencing decisions. This is a hands-on role for someone who can move between business questions, event-level data, and statistical analysis and take ownership before every requirement has been defined.
Responsibilities
Own analytical quality and communication.
Marketing Analyst - Analyze marketing performance on behalf of clients, own attribution and marketing effectiveness analyses, turning measurement into client decisions.
our company Product Analytics - Understand how customers use our product, Guiding product decisions through evidence while Building a reliable foundation for product measurement.
Panel Data & AI-Chat Behavioral Modeling - Analyze how people use AI chats, modeling it using intent taxonomies, behavioral segments, etc. Produce robust, representative insights.

We're looking for a Data Warehouse Tech Lead to drive the technical vision and execution of our data infrastructure that powers decision-making across.
You'll lead both the technology and the business coordination for our data warehouse - architecting scalable solutions while working closely with stakeholders and data providers to ensure our platform serves the entire organization's needs. This role combines deep technical leadership with strategic business partnership as we build our next-generation data stack.
We believe three things matter for every role: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.
Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role you'll:
Lead technical architecture - design and develop scalable data warehouse solutions that support multiple products and serve the entire organization's analytics needs
Manage the technical roadmap - set strategy and guide execution for the Data Warehouse team, ensuring our platform evolves with business requirements
Drive business process coordination - translate business needs into technical requirements while establishing clear data contracts with R&D, Analytics, and external data providers
Establish and implement best practices - set technical standards for data warehouse architecture, performance tuning, and development methodologies that guide the entire team's approach to building scalable data solutions
Create and maintain sustainable data pipelines - build resilient systems capable of handling unstructured data and managing an evolving schema registry across diverse data sources
Implement advanced data modeling - create robust data structures using methodologies like dimensional modeling, and optimize ETL/ELT processes for our semantic layer
Establish data quality standards - build processes for schema evaluation, anomaly detection, and monitoring data completeness and freshness across all sources
Lead cross-team collaboration - work directly with Data Engineers, ML Platform Engineers, Data Scientists, Analysts, and Product Managers to align technical solutions with business goals

We are looking for a brilliant Data Engineer- an independent, logical thinker who enjoys solving complex problems and building scalable data solutions. You will own and evolve the data foundations that power Product, Finance, Operations, and Business teams. Working closely with diverse stakeholders, Analysts, and R&D teams, you will serve as a technical partner, translating complex business challenges into robust data models, pipelines, and infrastructure. You should be able to translate business and data requirements into scalable data pipelines and data models, integrating multiple internal and external data sources using the most appropriate technologies. Beyond building pipelines, you will improve data quality, modernize existing data assets, automate manual workflows, and help create a clean, reliable, and scalable data ecosystem that supports the company's growth.
Roles and Responsibilities:
Design and build end-to-end data pipelines - From defining source structures and integrating APIs to delivering clean, trusted datasets that enable analytics, reporting, and operational workflows.
Translate business needs into scalable data solutions based on business priorities and the product roadmap, understand technical requirements, and deliver purpose-built pipelines and tools.
Act as the technical partner for Product, Finance, Operations, and R&D teams, translating business needs into scalable data models, pipelines, and internal tools while ensuring reliable, trusted data across the organization.
Write high-quality, maintainable code, while following best practices and leveraging modern data tooling and CI/CD principles.
Drive data quality and reliability - Monitor, validate, troubleshoot, and continuously improve data quality and pipeline reliability across the data platform.

As a Senior Data Infra Engineer, you'll be a key part of our data platform team, responsible for designing, building, and maintaining robust and scalable data pipelines. You'll work closely with data scientists, analysts, and server side engineers to ensure our data is reliable, accessible, and ready for analysis. Your expertise will be crucial in expanding our data warehouse and data lake capabilities, enabling us to deliver next-generation ad tech solutions.

Your mission will be to:

Develop and Optimize Data Pipelines: Design, build, and maintain ETL/ELT pipelines using Apache Spark to ingest, process, and transform large-scale datasets from various sources.
Manage Cloud Infrastructure: Architect and manage our data infrastructure primarily on Google Cloud Platform (GCP) or Amazon Web Services (AWS). This includes services like BigQuery, S3, GCS, EMR, and AirFlow.
Enhance Data Storage: Improve and manage our data warehouse and data lake solutions, ensuring data quality, consistency, and accessibility for business intelligence and machine learning applications.
Collaborate and Innovate: Partner with cross-functional teams to understand data needs and implement solutions that support new product features and business initiatives.
Ensure Data Integrity: Implement monitoring, alerting, and logging systems to maintain data pipeline health and ensure data accuracy.

We are looking for a Senior Data Scientist to join a team of passionate engineers who build premium solutions at scale. Every day, we tackle ambiguous and stimulating challenges. We build and operate large-scale distributed machine learning systems that process 1B predictions per second during real-time auctions.

What will you do?
As a Senior Data Scientist, your mission will be to:

Improve existing algorithms & tools allowing to explore and analyze more and more data and provide accurate feedback on the Teads activities.
Develop new algorithms & new approaches to provide accurate predictions and drive new products by providing business insights.
Implement your algorithms and models end to end.
Collaborate with a variety of teams to develop services from design to production.
Make sure the software is in good hands by writing, running and automating tests (unit, functional, load...).
Keep up to date with the latest Machine Learning technologies to make sure we always use the best class algorithms according to the context.
Shape how billions of people discover and enjoy premium content by improving ad relevance, quality, and efficiency across Teads global publisher network.