Senior Software Engineer, Data Engineer

Maropost

Senior Software Engineer (Data Engineer) developing scalable data solutions for analytics and AI at Maropost. Collaborating across teams to deliver end-to-end engineering solutions with minimal dependencies.

Posted 7/8/2026full-timeRemote • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building scalable data engineering solutions, including data ingestion pipelines and analytics warehouses, while ensuring data governance and security. Proficient in modern data processing frameworks and cloud platforms, with strong problem-solving and collaboration skills.

Highest-signal resume keywords

Data Engineering SolutionsClickHouse ExpertiseData Ingestion PipelinesSQL SkillsCloud Platforms (GCP)

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Hard Skills

Data EngineeringSQLData ModellingBackend Services (Go, Python)Data GovernanceOLAP Workload OptimizationStreaming Technologies (Kafka, Pulsar)Infrastructure-as-Code (Terraform)Analytical Data WarehousesData Processing Frameworks

Soft Skills

Problem-SolvingCommunicationCollaborationProactive AttitudeSelf-Driven

Tools & Technologies

ClickHouseBigQuerySnowflakeAmazon RedshiftDebeziumFlinkDataflowPostgreSQLTimescaleDBAI-Powered Applications

Industry Keywords

Data Governance StandardsMulti-Tenant Security ControlsEvent-Driven ArchitecturesCDCSaaS Platforms

Tech Stack

Tools & technologies

Amazon RedshiftBigQueryCloudDistributed SystemsGoGoogle Cloud PlatformKafkaPostgresPulsarPythonSQLTerraform

About the role

Key responsibilities & impact

Design and build scalable data engineer 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 data pipeline 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

What you’ll need

5+ years of hands-on software engineering experience building and operating highly scalable distributed systems, data engineering solutions, 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 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.

Benefits

Comp & perks

Flexible work arrangements