- Engineering Manager
- Distributed systems enthusiast
- 🇬🇧 Based in the UK (London / Maidenhead orbit)
- 🏢 Engineering Manager @ Meltwater
- 🏗️ ~15+ years building backend and data systems
- 🎓 MSc Computer Science (Distinction), University of Edinburgh
- 🧵 I write occasionally when something bugs me enough → jairam.dev
- 🌐 https://jairam.dev — longer thoughts, fewer hot takes
- 🐙 https://github.com/jairamc — code and experiments
- 🐘 @[email protected] — occasional signals into the void
- Design and run cloud-native systems for large-scale data extraction
- Lead teams that ship production systems (and survive them)
- Translate:
vague idea → architecture → running system → pager alerts → fixes → lessons - Balance engineering, people, and the occasional reality check
If it stores, streams, or shards data, I’ve probably broken it at least once:
- Hadoop, HDFS, Hive
- Cassandra, HBase
- Kafka, RabbitMQ
- Elasticsearch
Built systems dealing with TB-scale data per day and the kind of edge cases that only show up at 3am.
- Java
- Scala
- Python
- AWS (EC2, ECS, Lambda, RDS)
- Docker
- Terraform
- Distributed systems
- Data pipelines
- Backend services
- Meltwater – Principal Engineer → Team Lead → Engineering Manager
- Built ML-driven content extraction systems on AWS
- QuantumBlack (McKinsey) – Platform Engineer
- Backend systems for data science platforms (Scala, Spark, Mesos)
- Datasift – Big Data Engineer → Team Lead
- Worked on systems processing ~2TB/day of social data
- Microsoft (earlier life) – Software Development Engineer
- Learned things properly the first time (mostly)
🧩 Engineering Beliefs
- Distributed systems are just failure modes wearing APIs
- Scale reveals design flaws with theatrical timing
- Most “technical” problems are coordination problems
- If it’s not observable, it’s already broken (you just don’t know yet)