Senior Software Engineer, Data Platform

Responsibilities

• Design and build scalable data platform services that power analytics, reporting, machine learning, and AI workloads across Maropost products.
• Build and maintain reliable data ingestion pipelines using CDC and event-driven architectures.
• Develop and evolve our centralized analytics warehouse, ensuring high performance, scalability, and maintainability.
• Design and implement data models, materialized views, and aggregation strategies to support product analytics and business reporting.
• Build supporting APIs and services that expose analytics and reporting capabilities to internal and external consumers.
• Define and implement multi-tenant security controls, data governance standards, and access management policies.
• Monitor platform health, data freshness, ingestion lag, and overall system reliability.
• Contribute to technical specifications and actively participate in architecture and design discussions.
• Improve developer productivity through automation, tooling, observability, and operational excellence.
• Strengthen test coverage and engineering practices to ensure reliable and maintainable systems.

Requirements

• 5+ years of hands-on software engineering experience building and operating highly scalable distributed systems, data platforms, or backend services in production.
• Strong experience with modern analytical data warehouses such as ClickHouse, BigQuery, Snowflake, or Amazon Redshift.
• Deep expertise in ClickHouse, including internals, materialized views, and OLAP workload optimization, is a plus.
• Experience designing and operating large-scale data ingestion pipelines using Kafka, Pulsar, CDC-based architectures, and related streaming technologies.
• Familiarity with tools such as Debezium, Flink, Dataflow, or similar streaming and data processing frameworks is preferred.
• Strong SQL skills with hands-on experience in data modelling, query optimization, and analytical workloads.
• Experience working on data platforms, data engineering, or analytics engineering initiatives involving large-scale data processing and transformation workloads.
• Experience building and maintaining backend services in Go (preferred) or another modern strongly typed programming language, along with proficiency in Python.
• Experience with cloud platforms, preferably GCP, including managed data, messaging, and observability services.
• Experience owning and delivering production systems end-to-end, from technical design and stakeholder discussions through deployment, operational support, and iterative improvements across multiple release cycles.
• Experience with multi-tenant SaaS platforms, data governance practices, data security controls, and infrastructure-as-code tools such as Terraform.
• Experience with analytical and time-series databases such as PostgreSQL, TimescaleDB, or similar technologies.
• Exposure to AI-powered applications, LLM integrations, or agentic workflows is an added advantage.
• Comfortable participating in on-call rotations and focused on building simple, efficient solutions without over-engineering.
• Proactive and self-driven, with strong problem-solving and communication skills, and the ability to collaborate effectively with both technical and non-technical stakeholders.