Data Engineer
Job Details
About the Company
With operational hubs scattered across Europe, Asia, and LATAM, and its headquarters situated in San Francisco, US, the company boasts a workforce of over 1,000 adept professionals. Spanning across more than 20 countries, ALLSTARSIT offers a diverse range of skilled employees across various verticals, including AI, cybersecurity, healthcare, fintech, telecom, media, and so on.
About the Project
Our client is a leader in AI-powered performance marketing, operating across 25+ verticals with unmatched precision, speed, and scale. Their proprietary technology stack integrates seamlessly with major media platforms, enabling real-time event-level data exchange, optimization, and attribution.
At the core of their operation is a deep commitment to AI-driven decision-making. From real-time bidding engines and predictive lead scoring to campaign automation and anomaly detection, their in-house AI models are central to how we scale campaigns, reduce inefficiencies, and outperform market benchmarks.
They’ve built and continue to evolve a robust internal platform to empower media buyers, analysts, and operators with real-time alerts, smart recommendations, and semi-autonomous optimization tools.
Role Summary
Builds high-performance, reliable data systems, including real-time pipelines, tracking, and Feature Store foundations — enabling both analytics and ML production.
Specialization
Headquarters
Years on the market
Team size and structure
Current technology stack
Required skills:
- Strong software engineering fundamentals (clean code, testing, system design)
- Proven experience building real-time, low-latency data services
- Experience operating in production at scale (high throughput, distributed systems)
- Strong background with modern data stack (e.g., Kafka/Kinesis, Spark/Flink/Beam, Snowflake/BigQuery/Redshift)
- Ability to design schemas, ingestion flows, and data quality frameworks
- Experience collaborating with ML teams on feature availability and consistency
Preferred Qualifications (Nice to Have)
- Experience supporting ML systems (feature serving, online/offline consistency)
- Production experience with model inference pipelines
- Strong DevOps experience (Docker, Kubernetes, CI/CD)
- Experience with cloud-native architectures (AWS/GCP)
- Experience implementing data observability / lineage frameworks
