Site Reliability Engineer for a social intelligence platform processing large-scale social data across major social networks. Enterprise clients across multiple industries.

Remote-first team with a strong distributed culture. TDD, trunk-based development, and pair programming as daily practice.

The Context #

The platform ingests and processes social data at a scale where every infrastructure decision has compounding consequences. Pipelines need to run reliably across large volumes of records. Costs grow fast if left unchecked. Compliance requirements add constraints to every architectural choice. And the data keeps growing.

What I Did #

Infrastructure and GitOps. Architected all infrastructure changes through Terraform with GitOps workflows. Designed multi-region AWS architecture and failover strategies. Automated systems that used to require manual intervention – deployments, scaling, incident response.

Data pipelines. Designed and maintained the infrastructure behind ETL/ELT pipelines processing large volumes of records daily. Deployed workflow orchestration replacing fragile cron jobs. Managed distributed processing through message queues. Built operational tooling in Python (boto3, FastAPI, pandas) for automation and metrics analysis. Set up CI/CD for data workflows, serverless functions, and background workers.

Search infrastructure. Supported search clusters for data engineers building semantic queries across massive datasets. Kept them fast and stable under production load.

Databases. Migrated databases to managed services with zero downtime. Optimized document database clusters for social graph queries. Implemented specialized indexes for advanced query patterns. Managed caching clusters for real-time performance. Automated backup strategies with aggressive RPO targets.

Compliance and security. Managed GDPR compliance for European user data – data retention, privacy controls, audit trails. Implemented SOC2 Type II controls and evidence collection. Led cloud provider compliance reviews. Automated compliance monitoring and evidence collection. Worked directly with the DPO and external auditors.

Observability. Implemented Prometheus and Grafana monitoring with proactive alerting. Tracked not just infrastructure health but data quality metrics and pipeline throughput. Cloud cost as an operational metric, not an afterthought.

FinOps. Cost and usage analysis as the primary tool for visibility and forecasting. Managed commitment-based discounts and negotiated agreements. Optimized compute strategies across container orchestration platforms. The result: significant cost reduction without sacrificing performance or reliability.

Lessons Learned #

1. Infrastructure as code is not optional at scale. Terraform and GitOps turned infrastructure changes from risky manual operations into reviewable, repeatable, reversible code. Without it, a platform this size drifts into chaos.
2. Managed services let small teams punch above their weight. Every service you don’t operate yourself is capacity your team spends on what actually matters. The migration cost pays for itself in operational headroom.
3. FinOps needs engineering, not just dashboards. Systematic cost analysis and commitment optimization cut costs more than any single architectural change.