Data Engineer II

MRSOOL | مرسول

. Design, build, and maintain scalable batch and real-time data pipelines using Maxwell, Kafka, Spark, and dbt .

Posted 9/17/2026full-timeRemote • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building scalable data platforms, data lakes, and data warehouses, with a strong focus on data quality, observability, and governance. Proficient in leveraging modern data architectures and cloud-native technologies to deliver trusted datasets and analytics solutions.

Highest-signal resume keywords

Apache Spark (Scala, Python)Data Pipeline DevelopmentCloud-Native Data PlatformsMedallion ArchitectureDbt Model Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLData ModelingPerformance TuningData Quality AssuranceDistributed Data ProcessingBatch and Streaming Data PipelinesData Warehouse DesignData Ingestion FrameworksAutomation and CI/CDEvent-Driven Architectures

Soft Skills

CollaborationProject OwnershipCommunication

Tools & Technologies

KafkaAmazon S3BigQueryTrinoDbtMetabase

Industry Keywords

Data LakesData WarehousesData GovernanceObservabilityData Lineage

Tech Stack

Tools & technologies

ApacheBigQueryCloudKafkaPythonScalaSparkSQL

About the role

Key responsibilities & impact

Design, build, and maintain scalable batch and real-time data pipelines using Maxwell, Kafka, Spark, and dbt
Develop and optimize Bronze, Silver, and Gold data models using Medallion Architecture
Build and maintain cloud-native data platforms using S3, Spark, Trino, and BigQuery
Design data ingestion frameworks using CDC, Kafka, and event-driven architectures
Create and maintain data warehouses and data marts for reporting and self-service analytics
Translate business requirements into scalable data solutions with Product, Analytics, Engineering, and Business stakeholders
Develop reusable dbt models, testing frameworks, and documentation
Optimize Spark jobs, Trino queries, and storage layouts
Own critical data pipeline lifecycles, monitoring, SLA adherence, and incident resolution
Build reusable data-platform frameworks, automation, CI/CD pipelines, and engineering practices
Ensure data quality through validation, monitoring, lineage, observability, security, and governance
Deliver trusted datasets, semantic models, and Metabase dashboards for decision-making

Requirements

What you’ll need

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
Experience building production-grade data pipelines and distributed data processing applications
Hands-on experience with Apache Spark, distributed data processing, performance tuning, and optimization
Experience building batch and streaming data pipelines using 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 with cloud-native data platforms and technologies such as Amazon S3, BigQuery, Trino, or similar analytics engines
Experience designing dimensional models, star schemas, and reliable data marts
Hands-on experience with dbt, reusable models, automated testing, and documentation
Knowledge of data quality, observability, lineage, and engineering best practices
Experience optimizing large-scale data pipelines, SQL queries, and distributed processing jobs
Familiarity with CI/CD, Git-based development workflows, infrastructure automation, and modern software engineering best practices
Ability to independently own projects from design through production
Experience collaborating across Product, Engineering, Analytics, and Business teams

Benefits

Comp & perks

Inclusive and diverse workplace
Remote work environment
Competitive compensation
Potential share options for certain roles
Regular training
Annual learning stipend
High degree of autonomy
Mentorship