דרושים » הנדסה » Senior Data Engineer

We are looking for a Senior Data Engineer.
As a Senior Data Engineer, youll be more than just a coder - youll be the architect of our data ecosystem. Were looking for someone who can design scalable, future-proof data pipelines and connect the dots between DevOps, backend engineers, data scientists, and analysts.
Youll lead the design, build, and optimization of our data infrastructure, from real-time ingestion to supporting machine learning operations. Every choice you make will be data-driven and cost-conscious, ensuring efficiency and impact across the company.
Beyond engineering, youll be a strategic partner and problem-solver, sometimes diving into advanced analysis or data science tasks. Your work will directly shape how we deliver innovative solutions and support our growth at scale.
Responsibilities:
Design and Build Data Pipelines: Architect, build, and maintain our end-to-end data pipeline infrastructure to ensure it is scalable, reliable, and efficient.
Optimize Data Infrastructure: Manage and improve the performance and cost-effectiveness of our data systems, with a specific focus on optimizing pipelines and usage within our Snowflake data warehouse. This includes implementing FinOps best practices to monitor, analyze, and control our data-related cloud costs.
Enable Machine Learning Operations (MLOps): Develop the foundational infrastructure to streamline the deployment, management, and monitoring of our machine learning models.
Support Data Quality: Optimize ETL processes to handle large volumes of data while ensuring data quality and integrity across all our data sources.
Collaborate and Support: Work closely with data analysts and data scientists to support complex analysis, build robust data models, and contribute to the development of data governance policies.

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructuresomeone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacksits about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakesdesigned for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.

Required Senior Data Engineer
The opportunity
Join our dynamic Data & ML Engineering team in iAds and play a pivotal role in driving data solutions that empower data science, finance, analytics, and R&D teams. As an Experienced Data Engineer, you'll work with cutting-edge technologies to design scalable pipelines, ensure data quality, and process billions of data points into actionable insights.
Success Indicators:
In the short term, success means delivering reliable, high-performance data pipelines and ensuring data quality across the product. Long-term, you'll be instrumental in optimizing workflows, enabling self-serve analytics platforms, and supporting strategic decisions through impactful data solutions.
Impact:
Your work will directly fuel business decisions, improve data accessibility and reliability, and contribute to the team's ability to handle massive-scale data challenges. You'll help shape the future of data engineering within a global, fast-paced environment.
Benefits and Opportunities
You'll collaborate with talented, passionate teammates, work on exciting projects with cutting-edge technologies, and have opportunities for professional growth. Competitive compensation, comprehensive benefits, and an inclusive culture make this role a chance to thrive and make a global impact.
What you'll be doing
Designing and developing scalable data pipelines and ETL processes to process massive amounts of structured and unstructured data.
Collaborating with cross-functional teams (data science, finance, analytics, and R&D) to deliver actionable data solutions tailored to their needs.
Building and maintaining tools and frameworks to monitor and improve data quality across the product.
Providing tools and insights that empower product teams with real-time analytics and data-driven decision-making capabilities.
Optimizing data workflows and architectures for performance, scalability, and cost efficiency using cutting-edge technologies like Apache Spark and Flink.

We're looking for a hands-on, business-minded Analytics Engineer to build the infrastructure and tooling that turns this data into operational gold.
Youll be the first data hire in our new Operations Insights team, responsible for designing the operations data platform, generating insights for internal and external stakeholders, and helping our business teams deliver measurable impact. This is a hybrid role that combines data engineering with analytics and product thinkingyoull build, analyze, and consult.
From designing the data warehouse to crafting performance dashboards and investigating edge-case customer issues, youll be the go-to person who makes data accessible, trustworthy, and powerful.
A day in the life:
Design and implement the operational data platform, consolidating data from R&D systems
Build and maintain data pipelines and tables for store performance, deployment health, and system diagnostics
Collaborate with the Director of Customer Value & Insights and Expansion Managers to define KPIs and surface business insights
Conduct exploratory data analysis to support tough customer and deployment challenges
Build internal tools and dashboards that empower teams to self-serve critical data
Investigate anomalies, edge cases, and complex failure scenarios to improve deployments
Maintain data quality, documentation, and observability as we scale to hundreds of stores

We are looking for our first dedicated Data Engineer a self-motivated and proactive professional with a strong can-do attitude and a sense of ownership. This role involves taking responsibility across all data domains within the company, working closely with our analytics and development teams to build and maintain the data infrastructure that supports business needs. This position is ideal for someone ready to independently lead data engineering efforts and make a meaningful impact.

Responsibilities:
Design, develop, and maintain scalable data pipelines and ETL workflows using tools such as Python, dbt, and Airflow.
Architect and optimize our data warehouse to support efficient analytics, reporting, and business intelligence at scale.
Model and structure data from multiple internal and external sources (such as Salesforce, Jira, Mixpanel, etc.) into clean, reliable, and analytics-ready datasets.
Collaborate closely with our systems architect, analytics, and development teams to translate business requirements into robust and efficient technical data solutions.
Monitor and optimize pipeline performance to ensure data completeness and scalability.
Serve as a key partner and subject-matter expert on all data-related topics within the team.
Implement data quality checks, anomaly detection and validation processes to ensure data reliability.

As a customer-centric tech company, we created an insurance experience that is smart, instant, and delightful.
youll be working with a group of like-minded makers, who get a kick out of moving fast and delivering great products. We surround ourselves with some of the smartest, most motivated, creative people who are filled with positive energy and good karma.

Unlike most publicly traded companies, were nimble and efficient. We take pride in the fact that we still think and operate like a startup. We dont care much about titles and hierarchy and instead focus on innovation, bold moves, and challenging the status quo.

Were built as a lean, data-driven organization that relies on a common understating of objectives and goals to provide teams with autonomy and ownership. We dont like spending our days in meetings and we skip committees altogether. theres no such thing as going over someones head. We have zero tolerance for bureaucracy, office politics, and lean-back personalities.

As a Public Benefit Corporation and a certified B-Corp, we deliver environmental and social impact using our products and tech. Through our Giveback program, we partner with organizations such as the ACLU, New Story, The Humane Society, Malala Fund, American Red Cross, 360.org, charity: water, and dozens of others, and have donated millions towards reforestation, education, animal rights, LGBTQ+ causes, access to water, and more.

We are looking for a Senior Data Engineer to join our Platform group in the Data Infrastructure team.
Youll work hands-on to design and deliver data pipelines, distributed storage, and streaming services that keep data platform performant and reliable. As a senior individual contributor you will lead complex projects within the team, raise the bar on engineering best-practices, and mentor mid-level engineers while collaborating closely with product, DevOps and analytics stakeholders.

As we continue to expand and evolve, were looking for a Data Engineer to join our growing team and help shape the future of trust and security in the decentralized world.
We have a large amount of varied, exciting, and unique data on our hands, and were already squeezing value out of it for our customersbut theres so much more in there. Your goal will be to help construct the data in ways that enable both our business users and research group to dig deeper. As our very first dedicated data engineer, youll have a huge impactbut youll also need the independence and proactiveness to own it.
What Youll Do:
Design and build complex data pipelines to ingest, process, and transform data from a variety of sources, especially logs and textual inputs.
Collaborate closely with Software Engineering and Product teams to ensure data is accessible and usable.
Develop efficient ETL processes using frameworks such as DBT, Airflow, or their equivalents.
Own and optimize your data environment (e.g., Snowflake), focusing on performance tuning, governance, and reliability.
Build dashboards for various company-wide use cases.
Implement best practices for data management, quality assurance, and security within cloud infrastructures (AWS).
Enable ML and analytics teams by building pipelines that feed feature stores and model training workflows.

We are looking for a hands-on Data Specialist to join our growing data group, working on the practical backbone of high-scale, financial-grade systems. Youll work closely with engineers, BI, product, and business stakeholders, expert in design, build, and optimize data pipelines and integrations in a cloud-native environment.
If you thrive on solving complex data challenges, enjoy getting deep into code, and want to make an impact on fintech infrastructure, wed love to meet you.
Your Day-to-Day:
Develop, maintain, and optimize robust data pipelines and integrations across multiple systems
Build and refine data models to support analytics and operational needs
Work hands-on with data orchestration, transformation, and cloud infrastructure (AWS/Azure)
Collaborate with engineering, BI, and business teams to translate requirements into scalable data solutions
Contribute to data governance, data quality, and monitoring initiatives
Support implementation of best practices in data management and observability

We are looking for a Senior Backend Engineer to join our AI/ML team. In this role, youll work closely with data scientists to transform cutting-edge machine learning models into scalable, production-ready services. You will take ownership of designing, building, and maintaining the backend systems that power our AI-driven features.

This is a key position that bridges the gap between data science and production engineering, ensuring high performance, reliability, and maintainability of our ML-powered products.

Responsibilities:
Collaborate with data scientists to understand modeling outputs and convert them into deployable services.
Design and develop robust, scalable backend systems and microservices to support AI use cases.
Own the deployment and monitoring of ML models in production (with CI/CD, logging, observability).
Implement data processing pipelines in support of model training and inference.
Ensure software adheres to best practices in architecture, testing, and documentation.
Optimize model inference for latency, throughput, and resource efficiency.
Contribute to design decisions and technical strategy alongside AI and infrastructure leads.