Data Engineer - USA

Photon•United States,

11h•$46,000 - $161,000•Onsite

About The Position

We seek innovative, entrepreneurial, and passionate engineers dedicated to delivering exceptional user experiences. Your passion and ownership mindset are as vital as the high engineering standards, code quality, and world-class architectural skills we uphold. We manage billions of mail messages with advanced backend systems and algorithms, including NLP, GenAI, and ML techniques. Our infrastructure extracts information, creates enhanced content, and integrates diverse data sources to amplify key insights. This work presents rewarding technical challenges for high-caliber engineers eager to tackle impactful problems.

Requirements

Strong foundation in computer science, data engineering, or a related field, with a focus on big data technologies and cloud platforms.
Proficient in programming languages such as SQL, Python, or Java, and have experience with big data frameworks like Hadoop, Spark, or Kafka.
Excel in using stream and batch processing frameworks and data visualization tools, and are skilled in implementing data governance practices.
Advanced skills in designing and implementing data pipelines, ensuring data quality and integrity.
Experience with AI development tools such as Cursor, Copilot, and Claude.
Strong analytical skills and a knack for solving complex data challenges using innovative approaches.
A team player who can work effectively with cross-functional teams, including tech leads and architects, to deliver high-quality data solutions.
Enthusiastic about leveraging data engineering to drive innovation and enhance user experiences.
Maintain high standards for data quality and are committed to delivering robust, scalable solutions.
Eager to stay updated with the latest advancements in data engineering and apply them to improve.
Bachelor's degree in Computer Science, Data Engineering, or a related field.
5+ years in data engineering, big data processing, or a related field.
Strong skills in SQL, Python, or Java, with experience in big data technologies like Hadoop, Spark, or Kafka.
Proficient in stream and batch processing frameworks and data visualization tools.
Skilled in implementing data governance practices.
Proficiency with public cloud platforms, especially Google Cloud Platform (GCP).
Demonstrated ability to solve complex problems and implement efficient, scalable solutions.
Experience working with international teams and cross-functional collaboration.
Strong verbal and written communication skills for effective collaboration with US-based teams.
Eagerness to learn and adapt to new data technologies and methodologies.

Nice To Haves

A Master's degree is a plus.
Prior experience working with email systems or related technologies is a plus.
Self-driven and detail-oriented, with a passion for tackling challenges.
Strong teamwork spirit, excellent communication skills, and the ability to multitask and manage expectations effectively.
Experience with public cloud platforms, especially Google Cloud Platform (GCP), is preferred.
Experience with specific technologies such as DataProc, Dataflow, Composer, Data Lake, Pub/Sub, and Looker Studio.

Responsibilities

Contribute to Mail Intelligence Mission: Support the development of AI/ML capabilities that enhance user personalization and insights.
Innovate and Personalize: Assist in building scalable solutions that identify user interests and habits, contributing to personalized user experiences.
Data Pipeline and Insights Development: Design and implement robust data pipelines using frameworks like DataProc, Dataflow, and Composer. Streamline data processing and orchestration, utilize Data Lake and Pub/Sub for storage and streaming, and create insightful visualizations with Looker Studio to support AI/ML initiatives.
Collaboration: Work closely with cross-functional teams in the USA to integrate data solutions that enhance user experiences.
Data Governance: Implement robust data governance practices to maintain data quality and compliance across the organization.
Optimization: Continuously improve data workflows for efficiency and scalability.
Technical Strategy: Collaborate with tech leads to align data strategies with business goals.
Feedback and Recovery: Implement mechanisms to handle data processing failures gracefully.
Tradeoff Management: Balance cost, quality, and speed in data solutions to optimize performance.