דרושים » דאטה » Senior Data Engineer

We're looking for a Senior Data Engineer to help build next-generation data platform - the lakehouse foundation that will power data processing across the entire product. This is not a "write pipelines on top of someone else's platform" role, and it's not a pure infrastructure role either. It's both, deliberately.

You'll own the platform end to end: the infrastructure it runs on (Spark on Kubernetes, Apache Iceberg, AWS Glue, Airflow), the frameworks and tooling that let dozens of other engineers build on it without reinventing the wheel, and the design of the data pipelines themselves. Everything you build becomes leverage for the teams around you - your abstractions, base images, CI/CD flows, and operational patterns are what make the platform usable at scale.

You'll also own one of the hardest ongoing trade-offs in a high-scale data platform: balancing cost and performance. Compute sizing, storage layout, partitioning and compaction strategy, job scheduling - every decision has a price tag and a latency profile, and you'll be the one making those calls with data.

This role is ideal for an engineer who is equally comfortable debugging a Spark executor OOM on Kubernetes at 10am, designing a clean Python framework API at noon, and modeling the cost impact of a table layout change in the afternoon.



What You'll Do

Platform & Infrastructure

- Design, deploy, and operate our Spark-on-Kubernetes compute platform, including autoscaling, resource tuning, and multi-tenancy considerations.

- Own the lakehouse storage layer built on Apache Iceberg and AWS Glue catalog - table design, partitioning, compaction, schema evolution, and retention.

- Build and operate orchestration on Airflow: DAG standards, deployment flows, environment promotion, and reliability.

- Own production operations of the platform: monitoring, alerting, incident response, and continuous hardening.

Frameworks & Developer Enablement

- Build the code frameworks, libraries, and templates that other engineers use to write pipelines - so that spinning up a new production-grade Spark job is measured in hours, not weeks.

- Define and enforce standards for pipeline structure, testing, observability, and deployment across teams.

- Own CI/CD for data workloads: image builds, artifact promotion, and GitOps-based delivery.

- Act as a technical partner to product and research teams building on the platform - your customers are other engineers.

Data Pipelines & Architecture

- Design and build scalable batch and streaming pipelines processing complex, high-volume datasets from diverse sources.

- Lead large-scale backfills and migration initiatives, ensuring data consistency and integrity across evolving storage and compute platforms.

- Design event-driven data flows over large-scale queue systems (Kafka) for reliable, efficient data movement.

Cost & Performance

- Continuously balance cost against performance: right-size compute, tune queries and jobs, optimize storage layout and file sizes, and choose the correct engine for each workload.

- Build cost visibility and attribution into the platform so trade-offs are made with data, not guesswork.

We are a fast-paced, technology-driven team where everyone's contribution impacts product success.

We are looking for a hands-on, business-minded AI Engineer to join our Data & AI Team.

This role is for someone who has already built and shipped real AI products in a company environment. You will work as an integral part of the Data & AI Team, partnering directly with business stakeholders to identify high-impact opportunities, translate business needs into technical solutions, and build AI products that automate internal processes and create immediate value.

The ideal candidate is a builder. You should be comfortable working with LLMs, AI agents, automation workflows, APIs, data pipelines, data warehouses, and internal company systems. You should also be comfortable taking ownership, asking sharp business questions, and moving ideas from concept to production.

Our Data Team has already built internal AI products at our company. Now we are looking for someone who can help take us to the next level.

What Were Looking For
The right person has built with AI in a real company environment and knows how to turn business needs into practical internal products. They should be comfortable working with stakeholders, understanding how teams operate, and identifying where AI can create meaningful value.

Because this role sits inside the Data & AI Team, they also need to be strong with data. That means working confidently with company data, data warehouses, pipelines, APIs, and the technical building blocks that make AI products reliable and useful.

This role is for someone hands-on, curious, and hungry to build. Someone who can combine AI, data, and business context to help our company move faster, automate smarter, and turn ideas into measurable business wins.

What Youll Do
Build end-to-end internal AI products, automations, agents, and workflows that solve real business problems across our company.
Work directly with business stakeholders to understand pain points, define requirements, and turn ideas into scalable AI-driven solutions.
Design, prototype, test, deploy, and maintain production AI products using LLMs, AI agents, APIs, automation frameworks, and internal company data.
Work hands-on with data tools and infrastructure, including Snowflake, ETLs, data pipelines, APIs, AI tools, and internal servers.
Identify high-impact manual processes and turn them into automated AI-driven business wins.
Evaluate new AI tools, frameworks, and agentic workflows, and apply them where they can improve productivity, decision-making, or business operations.
Help shape internal AI development best practices around reliability, usability, security, documentation, maintainability, and production readiness.