Mrsool

Data Engineer II

Vaga remota de Data Engineering com fit claro de localização do candidato.

Publicada11 de jul. de 2026

Países elegíveis1 país aceito

Sinal de senioridadeMiddle

Modelo de trabalhoRemoto

Locais aceitos para candidatos

Índia

CI/CD Python Spark SQL

Resumo da vaga

Data Engineer II

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

What You Will Do ❓

Design, build, and maintain scalable batch and real-time data pipelines using Maxwell, Kafka, Spark, and dbt to power analytics and business-critical applications.
Develop and optimize data models following Medallion Architecture (Bronze, Silver, Gold) to create reliable, reusable, and high-quality datasets.
Build and maintain cloud-native data platforms using S3, Spark, Trino, and BigQuery, ensuring scalability, reliability, and cost efficiency.
Design robust data ingestion frameworks leveraging CDC (Maxwell), Kafka, and event-driven architectures to support near real-time data processing.
Create, optimize, and maintain data warehouses and data marts that enable fast, reliable reporting and self-service analytics.
Partner closely with Product Managers, Data Analysts, Backend Engineers, and Business stakeholders to translate business requirements into scalable data solutions.
Develop reusable dbt models, testing frameworks, and documentation to improve data quality, governance, and developer productivity.
Optimize Spark jobs, Trino queries, and storage layouts for performance, reliability, and cost efficiency.
Own the end-to-end lifecycle of critical data pipelines, ensuring high availability, monitoring, SLA adherence, and proactive incident resolution.
Build and enhance the core data platform by developing reusable frameworks, automation, CI/CD pipelines, and engineering best practices.
Ensure data quality through validation, monitoring, lineage, and observability while implementing best practices for security and governance.
Enable analytics teams by delivering trusted datasets, semantic models, and dashboards that power decision-making through Metabase.

What We're Looking For 🚀

4+ years of hands-on experience designing and building scalable data platforms, data lakes, and data warehouses.
Strong proficiency in Spark (Scala, python) and SQL, with experience building production-grade data pipelines and distributed data processing applications.
Hands-on experience with Apache Spark and a solid understanding of distributed data processing, performance tuning, and optimization.
Experience building batch and streaming data pipelines using technologies such as Kafka, CDC/Maxwell, or similar event-driven architectures.
Strong understanding of modern data lake architectures, including Medallion Architecture, data modeling, partitioning, and storage optimization.
Experience working with cloud-native data platforms and technologies such as Amazon S3, BigQuery, Trino, or similar analytics engines.
Solid experience designing dimensional models, star schemas, and building reliable data marts that support analytics and business intelligence.
Hands-on experience with dbt, including developing reusable models, implementing automated testing, and maintaining documentation.
Strong knowledge of data quality, observability, lineage, and engineering best practices to build reliable and maintainable data products.
Experience optimizing large-scale data pipelines, SQL queries, and distributed processing jobs for performance, scalability, and cost efficiency.
Familiarity with CI/CD, Git-based development workflows, infrastructure automation, and modern software engineering best practices.
Excellent problem-solving skills with the ability to independently own projects from design through production.
Strong communication and stakeholder management skills, with experience collaborating across Product, Engineering, Analytics, and Business teams.
A passion for building scalable data platforms and continuously improving developer experience, platform reliability, and operational excellence.

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