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Cognite - Senior Data Platform Engineer

Cognite - Senior Data Platform Engineer

CogniteBangalore
30+ days ago
Job description

Job Description :

Function : Software Engineering Backend Development

SparkSpark Streaming

Cognite is revolutionising industrial data management through our flagship product, Cognite Data Fusion - a state-of-the-art SaaS platform that transforms how industrial companies leverage their data. We're seeking a Senior Data Platform Engineer who excels at building high-performance distributed systems and thrives in a fast-paced startup environment. You'll be working on cutting-edge data infrastructure challenges that directly impact how Fortune 500 industrial companies manage their most critical operational data.

Responsibilities :

  • High-Performance Data Systems : Design and implement robust data processing pipelines using Apache Spark, Flink, and Kafka for terabyte-scale industrial datasets.
  • Build efficient APIs and services that serve thousands of concurrent users with sub-second response times.
  • Optimise data storage and retrieval patterns for time-series, sensor, and operational data.
  • Implement advanced caching strategies using Redis and in-memory data structures.

Distributed Processing Excellence :

  • Engineer Spark applications with a deep understanding of Catalyst optimiser, partitioning strategies, and performance tuning
  • Develop real-time streaming solutions processing millions of events per second with Kafka and Flink.
  • Design efficient data lake architectures using S3 / GCS with optimised partitioning and file formats (Parquet, ORC).
  • Implement query optimisation techniques for OLAP datastores like ClickHouse, Pinot, or Druid.
  • Scalability and Performance :
  • Scale systems to 10K+ QPS while maintaining high availability and data consistency.
  • Optimise JVM performance through garbage collection tuning and memory management.
  • Implement comprehensive monitoring using Prometheus, Grafana, and distributed tracing.
  • Design fault-tolerant architectures with proper circuit breakers and retry mechanisms.
  • Technical Innovation :

  • Contribute to open-source projects in the big data ecosystem (Spark, Kafka, Airflow).
  • Research and prototype new technologies for industrial data challenges.
  • Collaborate with product teams to translate complex requirements into scalable technical solutions.
  • Participate in architectural reviews and technical design discussions.
  • Requirements :

    Distributed Systems Experience (4-6 years) :

  • Production Spark experience - built and optimised large-scale Spark applications with understanding of internals
  • Streaming systems proficiency - implemented real-time data processing using Kafka, Flink, or Spark Streaming
  • JVM Language expertise - strong programming skills in Java, Scala, or Kotlin with performance optimisation experience.
  • Data Platform Foundations (3+ years) :

  • Big data storage systems - hands-on experience with data lakes, columnar formats, and table formats (Iceberg, Delta Lake)
  • OLAP query engines - worked with Presto / Trino, ClickHouse, Pinot, or similar high-performance analytical databases
  • ETL / ELT pipeline development - built robust data transformation pipelines using tools like DBT, Airflow, or custom frameworks
  • Infrastructure and Operations :

  • Kubernetes production experience -deployed and operated containerised applications in production environments.
  • Cloud platform proficiency - hands-on experience with AWS, Azure, or GCP data services.
  • Monitoring and observability - implemented comprehensive logging, metrics, and alerting for data systems.
  • Technical Depth Indicators :

  • Performance Engineering - System optimisation experience - delivered measurable performance improvements (2x+ throughput gains).
  • Resource efficiency - optimised systems for cost while maintaining performance requirements.
  • Concurrency expertise - designed thread-safe, high-concurrency data processing systems.
  • Data Engineering Best Practices - Data quality frameworks -implemented validation, testing, and monitoring for data pipelines.
  • Schema evolution - managed backwards-compatible schema changes in production systems.
  • Data modelling expertise - designed efficient schemas for analytical workloads.
  • (ref : hirist.tech)

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