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Data Platform Engineer (Distributed Systems & Graph Analytics)
We’re looking for a Data Platform Engineer to take end-to-end ownership of large-scale data pipelines and distributed systems powering a next-generation Analytics platform.
You will design, build, and operate production-grade data pipelines , working across Spark, cloud infrastructure, APIs, and graph systems to ensure high-throughput, low-latency, and reliable data processing at scale.
What You’ll Own
Data Pipeline & Platform Engineering
- Design and build end-to-end data pipelines (batch + streaming)
- Own large-scale Apache Spark workloads and distributed data processing
- Implement data ingestion → transformation → serving layers
- Manage schema evolution, data contracts, and pipeline reliability
Distributed Systems & Scale
- Work on systems handling high-volume graph datasets (entities + relationships)
- Optimize for latency, throughput, and fault tolerance
- Design scalable architectures using Kafka / Spark / Flink / Beam
Cloud & Infrastructure
- Deploy and operate systems on GCP / AWS (GKE, Dataproc, Cloud Run, etc.)
- Build and maintain CI/CD pipelines for data and microservices
- Use Docker, Kubernetes, Terraform for infrastructure automation
Data Reliability & Observability
- Implement data quality checks, monitoring, and alerting
- Ensure data integrity across pipelines and services
- Build systems to detect drift, inconsistencies, and failures in production
APIs & System Integration
- Work with GraphQL / REST / gRPC APIs for data access layers
- Ensure seamless integration between data systems and application layers
What We’re Looking For
- You have atleast 4 years in Data Engineering / Platform Engineering / Distributed Systems
- Strong hands-on experience with: Apache Spark / Distributed data processing, Cloud platforms (GCP or AWS), Streaming systems (Kafka / Flink / Beam)
- Solid programming skills in Python / Java / Scala / Node.js
- Experience building and owning production data pipelines end-to-end
- Understanding of: Microservices architecture, Data modeling & large-scale system design
- Ability to debug and optimize systems in real production environments
Why This Role is Different
- You own systems , not just components
- You work on real scale (millions → billions of data points)
- You solve distributed systems + graph + real-time problems
- You operate close to production impact, not isolated dev work
- You influence architecture from day one in an early-stage environment
What You’ll Get
- High ownership, low bureaucracy environment
- Work on cutting-edge graph + AI-driven data systems
- Exposure to complex, real-world data problems (fraud, risk, intelligence)
- Fast growth with direct impact on core platform architecture
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