דרושים » דאטה » Data Engineer - Applied ML

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!

As a Senior Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.

Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.



Key Job Responsibilities and Duties:

Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.

Dealing with massive textual sources to train GenAI foundation models.

Solving issues with data and data pipelines, prioritizing based on customer impact.

End-to-end ownership of data quality in our core datasets and data pipelines.

Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.

Providing tools that improve Data Quality company-wide, specifically for ML scientists.

Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.

Acting as an intermediary for problems, with both technical and non-technical audiences.

Promote and drive impactful and innovative engineering solutions

Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

As a Data Engineer, youll collaborate with top-notch engineers and data scientists to elevate our platform to the next level and deliver exceptional user experiences. Your primary focus will be on the data engineering aspects-ensuring the seamless flow of high-quality, relevant data to train and optimize content models, including GenAI foundation models, supervised fine-tuning, and more.

Youll work closely with teams across the company to ensure the availability of high-quality data from ML platforms, powering decisions across all departments. With access to petabytes of data through MySQL, Snowflake, Cassandra, S3, and other platforms, your challenge will be to ensure that this data is applied even more effectively to support business decisions, train and monitor ML models and improve our products.


Key Job Responsibilities and Duties:

Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.

Dealing with massive textual sources to train GenAI foundation models.

Solving issues with data and data pipelines, prioritizing based on customer impact.

End-to-end ownership of data quality in our core datasets and data pipelines.

Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.

Providing tools that improve Data Quality company-wide, specifically for ML scientists.

Providing self-organizing tools that help the analytics community discover data, assess quality, explore usage, and find peers with relevant expertise.

Acting as an intermediary for problems, with both technical and non-technical audiences.

Promote and drive impactful and innovative engineering solutions

Technical, behavioral and interpersonal competence advancement via on-the-job opportunities, experimental projects, hackathons, conferences, and active community participation

Collaborate with multidisciplinary teams: Collaborate with product managers, data scientists, and analysts to understand business requirements and translate them into machine learning solutions. Provide technical guidance and mentorship to junior team members.

We are looking for a Data Engineer to help build and scale the data platform behind our search quality, ML pipelines, product analytics, and business operations.
In this role, you will contribute across the full data lifecycle: ingesting data from production systems, designing and evolving our data warehouse, building batch and streaming pipelines, and making high-quality datasets available to researchers, engineers, analysts, and product teams across the company.
The platform spans tens of terabytes and ingests data from tens of proprietary and third-party sources - including our search engine and its components, CRM, billing, identity, and product analytics across multi-region production environments. Around 100 internal users rely on it daily.
You will work closely with engineers and stakeholders across the company, contribute to architectural and modeling decisions, and help improve the reliability, usability, and scalability of the data platform as it grows.

In this position, your responsibility will be to:
Contribute to the design, development, and operation of Tavily's data platform - from real-time ingestion through data warehouse medallion layers to consumer-facing datasets and dashboards.
Build and maintain reliable batch and streaming pipelines that ingest data from production services and external systems.
Design and evolve scalable, analytics-ready data models in the data warehouse.
Work closely with engineers across the company to ensure data produced by production systems is reliable, well-structured, and usable downstream.
Improve observability across the data platform, including data quality checks, freshness monitoring, lineage, schema evolution, and cost controls.
Partner with researchers, engineers, analysts, finance, and product managers to deliver trustworthy datasets for product, search quality, ML, and GTM analytics.
Contribute to defining the objects, entities, and relationships that represent Tavily's search domain - including agent inputs, URLs, chunks, agent sessions, crawls, and the connections between them - and translate them into clean, queryable data models.
Improve engineering practices around testing, documentation, deployment, and incident response.
Investigate and resolve production data issues, including broken pipelines, corrupted datasets, schema changes, and large-scale backfills.
Contribute to technical standards and best practices for data engineering across the company.
Help maintain high standards of data quality, integrity, security, and governance across environments.

We are looking for a Senior Data Engineer to build and scale the data foundation behind platform and products. You will own complex data end-to-end - from ingestion and transformation through modeling, quality, observability, and production delivery.
This is a hands-on senior IC role for a strong builder who can solve difficult data problems independently, set a high technical bar, and collaborate closely with engineering, DS, and product. You will turn large, fragmented datasets into reliable, reusable capabilities that power every product.
Responsibilities
Build and own scalable data pipelines- Design, implement, and operate robust pipelines for high-volume structured and unstructured data, with validation, monitoring, lineage, and recovery built in.
Scale the platform for growth- A key near-term initiative is re-architecting the system to support a significantly larger customer base. You will own performance and cost-efficiency across pipelines and services, keeping reliability and operating costs under control as the platform scales.
Build across the stack- This is not a pipelines-only role. You will also write backend services and some frontend, including the internal backoffice the team runs on. We hire builders, not narrow specialists.
Own the core data tables- Own schema design and evolution, data contracts, and the modeling standards the team follows - naming, shared dimensions, normalization, documentation. Be accountable when a table is wrong, late, or drifting.
Level up the teams data work- Pair with and advise software engineers and data scientists on Spark, SQL, and modeling, and help turn notebook-grade code into production-grade pipelines.
Partner cross-functionally- Translate product, client, compliance, and business requirements into clear technical designs and dependable production systems.

We are looking for an experienced and visionary Data Engineer to help design, build, and scale our BI platform.
In this role, you will be responsible for developing our data analytics platform - enabling scalable data pipelines and robust data modeling to support real-time and batch analytics to provide insights that serve both business intelligence and product needs.
You will be part of the R&D team, collaborating closely with engineers, analysts, and product managers to deliver a modern data architecture that supports internal dashboards and future-facing operational analytics.
If you enjoy turning raw data into powerful insights and owning the full data lifecycle, this role is for you!
Responsibilities:
Take full ownership of the design and implementation of a scalable and efficient BI data infrastructure, ensuring high performance, reliability, and security.
Design and architect data products, from ingestion to transformation, modeling, storage, and access.
Build and maintain ETL/ELT pipelines, batch and real-time, to support analytics, reporting, and product integrations.
Establish and enforce best practices for data quality, lineage, observability, and governance to ensure accuracy and consistency.
Integrate modern tools and frameworks such as Airflow, Databricks, Power BI, and streaming platforms.
Collaborate cross-functional with product, engineering, and analytics teams to translate business needs into data infrastructure.
Promote a data-driven culture - be an advocate for data-driven decision-making across the company by empowering stakeholders with reliable and self-service data access.

As a AI-Data Engineer, youll take a leading role in building the foundation of our companys Agentic Exposure Management platform: the source of truth that tells a security team what they have, whats actually risky and why, how to fix it, and who is responsible.
Enterprises run their security across a sprawl of cloud and on-prem tools. Making sense of that sprawl is the heart of what we do. Were building a complex data fabric that reconciles data from a hundred different sources, applies sophisticated transformation and enrichment, and turns partial, uncertain signal into one trustworthy model of an organizations real exposure.
Key Responsibilities:
Model the security graph. Design an ontology that spans both the modern cloud and the classical on-prem world, abstract enough to absorb 100+ different integrations under one model, yet rich enough to carry everything needed for decision making. Decide whats an entity, whats an edge, and whats a denormalized attribute, across millions of assets and finding rows.
Own asset identity engineering and reconciliation. Upstream identifiers are uncertain and volatile. Engineer deterministic identity resolution for assets and findings arriving from different vendors.
Build LLM-based and agentic pipelines for data classification, entity extraction, and enrichment, optimizing for cost, latency, and precision in production.
Build the integration factory. Stand up a scalable integration platform that drives down time-to-integration.
Build and maintain complex ETL pipelines handling millions of records: incremental sync, complex correlation, and data quality observability and monitoring.

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 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.

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.