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hirist

Member of Technical Staff - Distributed Systems/Data Infrastructure

Tidyhire
9 - 18 Years
Karnataka

Posted on: 01/10/2026

Job Description

Role:

As MTS on this team, you will contribute to the systems that power this platform - from computation graph management to pipeline optimization and dataset lifecycle. Your work will directly reduce operational waste across the company's data infrastructure.

What you'll do:

- Own and deliver major components - from design through production rollout.

- Design and evolve distributed systems powering ingestion, streaming, lakehouse/warehouse, catalog, and governance.

- Contribute to long-term architecture through design reviews and authoring architecture design documents, ensuring scalability and resilience.

- Build systems that balance latency, correctness, and cost while ensuring security and compliance.

- Drive operational excellence for services you own, including observability and incident response.

- Collaborate across product, infra, and analytics teams to align execution with business needs.

- Learn and grow in areas like governance, orchestration, and privacy engineering.

What you bring:

- Experience designing large-scale distributed systems (compute, storage, APIs, streaming).

- Ability to independently deliver complex projects from requirements to production.

- Systems thinker who anticipates bottlenecks, schema evolution, and reliability issues.

- Strong communication skills to influence cross-team technical outcomes.

- Growth mindset with curiosity to learn new technologies.

Tech Stack & Qualifications:

- 10+ years of software engineering experience, primarily in distributed systems or data platforms.

- Proficiency in Java/Python, CI/CD, and containerized environments.

- Hands-on expertise in tools like Kafka/Flink, Spark, Delta/Iceberg, Kubernetes, NoSQL/columnar stores.

- Strong foundation in algorithms and distributed design.

- BS/MS in CS or equivalent experience.

What you can expect:

- Impact at scale: powering global analytics and ML systems.

- Challenging problems: streaming, freshness/correctness, and multi-cloud resiliency.

- Collaborative culture that values inclusion and knowledge sharing.

- Support & growth: flexibility, benefits, and career development resources.

- Focus on reliability and sustainable on-call practices.

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