Data Engineer

Come [legally] hack with us on the data on the largest exchange that’s running our world. Not NASDAQ; the one with way more events - the Global Ads Exchanges, where millions of ads are born and clicked every second.

Step behind the curtain of algorithms and competitors that move $1T of annual budgets. Plunge into a world of ISP volumes of traffic, sub-second predictions, and TBs of live, real-world data. Use cutting-edge analysis, ML and engineering or just plain hacker-like thinking to outperform the market.

Arpeely is a Data-Science startup, leveraging data analysis, ML, engineering, mandulti-disciplinary thinking to gain a market edge and exploit hidden opportunities in real-time advertising. Processing over 350k requests per second and serving over 20B sub-second predictions daily, we build and operate Machine Learning algorithms running on the world’s largest Real-time-bidding (RTB) Ad-Exchanges. Arpeely is a Google AdX vendor and serves clients spanning from startups to Fortune-50 companies.

As our Data Engineer you will handle Big Data in real time, and become the owner and gatekeeper of all data and data flows within the company. You will bring data ingenuity and technological excellence while gaining a deep business understanding.

This is an amazing opportunity to join a multi-disciplinary A-team while working in a fast-paced, modern cloud, data-oriented environment.

What You’ll Do:

Implement robust, reliable and scalable data pipelines and data architecture.
Own and develop Arpeely DWH (Petabytes of data!).
Own the entire data development process, including business knowledge, methodology, quality assurance, and monitoring.
Collaborate with cross-functional teams to define, design, and ship new features.
Continuously discover, evaluate, and implement new technologies to maximize development efficiency.
Develop tailormade solutions as part of our data pipelines.
Lead complex Big Data projects and build data platforms from scratch.
Work on a high-scale, real-time, real-world business-critical data stores and data endpoints.
Implement data profiling to identify anomalies and maintain data integrity.
Work in a results-driven, high-paced, rewarding environment.

Requirements:

5+ years experience as a Data Engineer.
Good working knowledge of Google Cloud Platform (GCP).
Experience using AI-assisted tools or automation to improve data development, monitoring, or debugging workflows.
Strong experience in SQL and Python.
Experience with high volume ETL/ELTs tools and methodologies - both batch and real-time processing.
Understanding of how to build robust and reliable solutions.
The ability to understand the business impact of the data engineering tasks.
Hands-on experience in writing complex queries and optimizing them for performance.
Able to understand complex data and data flows.
Have a strong analytical mind with proven problem-solving abilities.
Ability to manage multiple tasks and drive them to completion.
Independent and proactive.