דרושים » תוכנה » Data Warehouse Tech Lead

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.

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 seeking talented and passionate Senior Data Engineer to join our Data team. In this pivotal role, you will be instrumental in designing, building, and optimizing the critical data infrastructure that underpins innovative creative intelligence platform. You will tackle complex data challenges, ensuring our systems are robust, scalable, and capable of delivering high-quality data to power our advanced AI models, customer-facing analytics, and internal business intelligence. This is an opportunity to make a significant impact on our product, contribute to a data-driven culture, and help solve fascinating problems at the intersection of data, AI, and marketing technology.

Key Responsibilities
Architect & Develop Data Pipelines: Design, implement, and maintain sophisticated, end-to-end data pipelines for ingesting, processing, validating, and transforming large-scale, diverse datasets.
Manage Data Orchestration: Implement and manage robust workflow orchestration for complex, multi-step data processes, ensuring reliability and visibility.
Advanced Data Transformation & Modeling: Develop and optimize complex data transformations using advanced SQL and other data manipulation techniques. Contribute to the design and implementation of effective data models for analytical and operational use.
Ensure Data Quality & Platform Reliability: Establish and improve processes for data quality assurance, monitoring, alerting, and performance optimization across the data platform. Proactively identify and resolve data integrity and pipeline issues.
Cross-Functional Collaboration: Partner closely with AI engineers, product managers, developers, customer success and other stakeholders to understand data needs, integrate data solutions, and deliver features that provide exceptional value.
Drive Data Platform Excellence: Contribute to the evolution of our data architecture, champion best practices in data engineering (e.g., DataOps principles), and evaluate emerging technologies to enhance platform capabilities, stability, and cost-effectiveness.
Foster a Culture of Learning & Impact: Actively share knowledge, contribute to team growth, and maintain a strong focus on how data engineering efforts translate into tangible product and business outcomes.

We are looking for an experienced Data Engineer to own and evolve data infrastructure and analytics platform.

You will design and build scalable data pipelines and data models, working with large and diverse datasets generated across Trigos global retail deployments. You will own the data lifecycle from ingestion and transformation through orchestration, reliability, and analytics-ready datasets.

As a key technical owner of data platform, you will also work closely with analysts, Product, R&D, Operations, and Customer Success to build trusted data products, evolve our BI semantic layer, and turn complex data into scalable solutions

A day in the life
Design, build, and maintain scalable ETL/ELT pipelines processing large and diverse datasets.
Own and evolve data architecture, from ingestion and transformation through analytics-ready data models and consumption.
Build reliable data workflows and orchestration processes, with a focus on scalability, performance, and maintainability.
Design and maintain curated data models that power analytics, operational workflows, and data-driven products.
Ensure data quality, observability, monitoring, and reliability across datasets and pipelines.
Develop and evolve the BI semantic layer, enabling trusted metrics and self-service analytics across the company.
Work closely with R&D and Product on data integrations and new data-driven capabilities, including use cases that bridge offline analytics and production systems.
Partner with analysts and business stakeholders to translate complex requirements into scalable data solutions.

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.

You and your team will work primarily with dbt, BigQuery, Looker, and GitHub, while using AI-enabled tools such as Cursor, MCP integrations, n8n, and OpenClaw to automate workflows and scale how the team works.

Success in this role requires strong leadership, technical depth, business understanding, and the ability to turn complex data into clear, actionable recommendations.

Key Responsibilities
Lead, manage, and develop a team of Data Analysts, including setting goals, providing feedback, and supporting their professional growth.
Own the teams priorities and analytical roadmap, ensuring the team focuses on the highest-impact business needs.
Define and drive technical and analytical standards across data modeling, SQL, dbt, testing, code reviews, and BI.
Partner with stakeholders and senior leadership to understand business needs, define KPIs, and translate data into actionable recommendations.
Lead high-impact analyses and guide the development of reliable, scalable data models and Looker dashboards.
Ensure data quality and lead investigations into anomalies, tracking issues, and inconsistent metrics.
Drive AI-native ways of working by turning repeatable analyses and processes into reusable, automated workflows.
Stay hands-on with data, code, and analysis when tackling complex or high-impact problems.

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.

We're hiring a Data Engineer to architect and own the systems that turn raw signal from merchants, customers, and experiments into something the whole company can build on.

Data is at the heart of everything we do - it powers our AI models, drives merchant-facing analytics, and informs how we make product decisions every day.

This is a high-ownership role for someone who wants to shape data architecture from the ground up- not inherit someone else's roadmap. You'll be the person who makes sure our data is fast, trustworthy, and easy for every team to use.

What You'll Do

Architect and build scalable data pipelines processing millions of events daily from merchant stores, product usage, experiments, and third-party integrations

Own our data warehouse strategy end-to-end- reliability, performance, and scalability as we grow

Define our event architecture and set the standards for how the company captures and models product and business data

Build data quality practices- monitoring, validation, testing, and alerting- that the rest of the team can trust

Partner directly with Product and Engineering to unlock the data behind our AI features, merchant analytics, and experimentation

Build self-service tooling so other teams can query and analyze data without depending on you as a bottleneck

Set the bar for data modeling, orchestration, and documentation across the org

Required Senior Data Engineer
Description
We are making the future of Mobility come to life starting today.
We support the worlds largest vehicle fleet operators and transportation providers to optimize existing operations and seamlessly launch new, dynamic business models - driving efficient operations and maximizing utilization.
At the heart of our platform lies the data infrastructure, driving advanced machine learning models and optimization algorithms. As the owner of data pipelines, you'll tackle diverse challenges spanning optimization, prediction, modeling, inference, transportation, and mapping.
As a Senior Data Engineer, you will play a key role in owning and scaling the backend data infrastructure that powers our platform-supporting real-time optimization, advanced analytics, and machine learning applications.
What You'll Do:
Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark, and other modern tools.
Own the backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets.
Leverage Google Cloud Platform (GCP) services to architect and operate scalable, secure, and cost-effective data solutions across the pipeline lifecycle.
Develop and optimize ETL/ELT workflows across multiple environments to support internal applications, analytics, and machine learning workflows.
Build and maintain data marts and data models with a focus on performance, data quality, and long-term maintainability.
Collaborate with cross-functional teams including development teams, product managers, and external stakeholders to understand and translate data requirements into scalable solutions.
Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability.

a central part of the R&D department is seeking a versatile Senior Data Engineer to take part in leading data vision using a lake-house architecture, and building high-scale solutions for handling and consuming the different data assets of the company.
In this role youll go beyond traditional boundaries, taking on full product responsibilities - from conceptualization and architecture to design, maintenance, and continuous development. You will collaborate closely with stakeholders across the company to ensure our solutions effectively meet their needs.
The ideal candidate will have strong data engineering capabilities, along with software development skills including understanding of designing and managing AWS cloud infrastructure and strong knowledge of different data architectures, methods and tools.
Responsibilities:
Design and development of scalable, reliable, and secure data infrastructure and build ETL pipelines that handle diverse clinical data for research. Write production SQL, Spark jobs and craft schemas that evolve gracefully as research and production questions change.
Automate releases with CI/CD and Infrastructure as Code.
Optimise throughput, latency and cloud cost to meet research timelines at large scale.
Develop and maintain high-quality, scalable, and efficient code while fostering a culture of continuous improvement through regular retrospectives and knowledge sharing.
Take ownership of the full development lifecycle, including requirement gathering, design, implementation, testing, deployment, and ongoing maintenance.
Collaborate with cross-functional teams, including data scientists, data analysts, product managers, regulatory teams, and other developers, to drive the development of new tools and features that support mission.
Explore and adopt new technologies and frameworks that can enhance the capabilities of the Data-Nexus team and the overall data projects .