Senior Software Engineer

Cognite
India

Who this role is best for

Best suited to engineers with distributed systems experience working in industrial data infrastructure.

Best fit for

  • Candidates with 6–12 years of experience in distributed data systems and a background in backend or platform engineering
    — “6–12 years of experience in distributed data systems, backend engineering, or platform engineering.”
  • Individuals who have built fault-tolerant systems and contributed to open-source projects
    — “Drive technical innovation through research, prototyping, and contributions to open-source.”
  • Professionals with hands-on experience in cloud platforms and large-scale data processing
    — “Experience with cloud platforms (AWS/GCP/Azure) and data lake/object storage.”

Things to consider

  • The role demands deep knowledge of Spark internals and JVM performance optimization.
    — “Deep expertise in Spark internals, including Catalyst optimizer, partitioning, and performance tuning.”
  • This position requires working in a fast-paced startup environment with high ownership expectations.
    — “You'll be working on cutting-edge data infrastructure challenges that directly impact how Fortune 500 industrial companies manage their most critical operational data.”

How to stand out

  • Highlight your experience with real-time streaming and fault-tolerant system design in your resume.
    — “Engineer distributed processing solutions, including real-time streaming that handles millions of events per second.”
  • Emphasize your background in optimizing OLAP query performance using systems like ClickHouse or Druid.
    — “Optimize OLAP query performance across systems such as ClickHouse, Pinot, or Druid.”
  • Showcase your ability to design cost-efficient data lake architectures with modern formats.
    — “Design and evolve cost-efficient data lake architectures (S3/GCS) using modern formats like Parquet/ORC.”
  • Demonstrate your knowledge of distributed tracing and retries in your interview responses.
    — “Build fault-tolerant, highly-available systems with circuit breakers, retries, and distributed tracing.”
  • Position yourself as a collaborative engineer who translates complex data problems into scalable solutions.
    — “Collaborate with product and architecture teams to translate complex industrial data problems into scalable, production-grade solutions.”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • build high-performance data pipelines
  • develop low-latency APIs
  • optimize time-series data storage
  • engineer distributed processing solutions
  • design cost-efficient data lake architectures
Typical background
distributed data systemsbackend engineeringplatform engineering

Skills & requirements

Required

Distributed SystemsHigh-performance Data PipelinesAPI DevelopmentData Storage And RetrievalReal-time StreamingData Lake ArchitectureOLAP Query OptimizationFault-tolerant SystemsMonitoring And ObservabilityArchitectural ThinkingCollaborative Mindset

Preferred

Low-code AI AgentsOpen-source ContributionsIndustrial Data/ai Platforms

Stack & domain

SparkFlinkKafkaScalaJavaPythonAWSGCPAzureS3GCSParquetORCClickhousePinotDruidRedisPrometheusGrafanaOpentelemetryLeadershipCollaborationProblem SolvingData InfrastructureDistributed SystemsBackend EngineeringPlatform Engineering

About the role

Original posting from Cognite via Greenhouse

What Cognite is Relentless to Achieve

Cognite operates at the forefront of industrial digitalization, building AI and data solutions that solve the world’s hardest, highest-impact problems. Our Moonshot is bold: unlock $100B in customer value by 2035, and redefine how global industry works.

About The Role

We're seeking a Senior Software 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.

How you’ll demonstrate Ownership

Build high-performance data pipelines using Spark, Flink, and Kafka to process terabyte-scale industrial datasets.

Develop low-latency APIs and services supporting thousands of concurrent users with sub-second response times.

Optimize time-series, sensor, and operational data storage and retrieval for massive scale.

Engineer distributed processing solutions, including real-time streaming that handles millions of events per second.

Design and evolve cost-efficient data lake architectures (S3/GCS) using modern formats like Parquet/ORC.

Optimize OLAP query performance across systems such as ClickHouse, Pinot, or Druid.

Build fault-tolerant, highly-available systems with circuit breakers, retries, and distributed tracing.

Drive technical innovation through research, prototyping, and contributions to open-source.

Collaborate with product and architecture teams to translate complex industrial data problems into scalable, production-grade solutions.

The Impact you bring to Cognite

Deep expertise in Spark internals, including Catalyst optimizer, partitioning, and performance tuning.

Strong hands-on experience with Flink/Kafka for both streaming and batch workloads.

Proven ability to scale systems to 10K+ QPS with strong foundations in JVM performance, GC tuning, and memory management.

Experience building high-throughput REST/gRPC services and advanced caching with Redis/in-memory data structures.

Strong understanding of monitoring and observability (Prometheus, Grafana, OpenTelemetry).

Solid architectural thinking with an ability to design distributed, secure, and resilient platforms.

Collaborative mindset with the ability to work across engineering, product, and platform teams.

Required Qualifications

6–12 years of experience in distributed data systems, backend engineering, or platform engineering.

Strong programming skills in Scala, Java, or Python.

Experience with cloud platforms (AWS/GCP/Azure) and data lake/object storage.

Prior experience with large-scale data, OLAP systems, or industrial/IoT data is a plus.

Contributions to open-source or experience in industrial data/AI platforms is an added advantage.

What we offer:

Opportunity to build world-class Industrial DataOps systems powering global energy, manufacturing, and industrial AI customers.

A fast-growing, high-ownership environment in Cognite’s India R&D Center.

Work on cutting-edge distributed systems, streaming architectures, AI-ready data platforms, and industrial-scale challenges.

A culture that rewards innovation, autonomy, continuous learning, and open-source contribution.

Competitive compensation, equity, and benefits aligned with Cognite’s global standards.

Why Cognite

You will work at the forefront of Industrial AI, partnering with some of the world's leading industrial organisations to solve problems that actually matter at scale — not hypothetical ones. You'll join a collaborative, fast-moving environment where curiosity and ownership are the norm, not the exception. If you want delivery work you can point to and be proud of, we'd love to hear from you.

Equal Opportunity

Cognite is committed to creating a diverse and inclusive environment at work and is proud to be an equal opportunity employer. All qualified applicants will receive the same level of consideration for employment.

Source: Cognite careers (Greenhouse)

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