Senior Software Engineer

Senior Software Engineer - Microservices and Data Pipelines, driving enterprise-grade data services and. We are looking for hands-on, self-driven people who are ready to explore and learn different technologies and languages to get things done. People who have previous experience with writing high through put micro services, large scale datasets/databases, ETL pipelines, Data engineering, connector frameworks with strong Python, data management, and compliance-minded delivery at scale would be a good fit for this position.

Key Responsibilities

• Design, implement, and operate Python-based microservices and high-throughput ETL pipelines that meet enterprise SLAs, RPO/RTO targets, and data quality standards
• Embed observability by default through structured logging, metrics, and distributed tracing; define SLOs, error budgets, and incident response runbooks for production services
• Conduct design and code reviews, performance and cost optimizations, and challenge fellow engineers to raise the bar on engineering excellence and operational maturity
• Design and evolve reusable connector frameworks for inbound and outbound integrations with Ad tech platforms like Facebook Ads, Google Ads, and other enterprise systems, emphasizing modularity, versioning, and backward compatibility
• Collaborate with Product, SecOps, and Platform teams to deliver scalable, cost-efficient solutions across Kubernetes, message buses, and workflow orchestrators

Required Qualifications

• 5+ years in backend/data engineering building Python services and ETL pipelines for enterprise environments with strict uptime, compliance, and performance requirements
• Expert Python skills, including async I/O, concurrency, and service frameworks, plus strong testing strategies, and interface contracts for maintainability at scale
• Proven microservices experience with containers and orchestration (Docker, Kubernetes) and event-driven patterns using message brokers or streaming platforms
• Robust SQL skills integrating with analytics warehouses such as Snowflake, including cost/performance tuning patterns
• Production-grade integrations with APIs, including rate-limit handling, retries, backoff policies, and change-data capture or incremental sync designs
• Deep observability practice using metrics, logs, and traces and experience implementing Open Telemetry instrumentation and alerting workflows

Preferred Qualification

• Experience in leveraging Apache Spark for efficient processing and management of large-scale distributed data workloads
• Strong understanding of modern Lakehouse architectures that unify data lakes and warehouses
• Exposure to serverless data patterns (e.g., AWS Lambda/Step Functions)