Data Engineer II

MRSOOL | مرسول

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

Posted 9/18/2026full-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 engineering best practices. Proficient in leveraging modern data architectures and cloud-native technologies to deliver reliable data solutions and insights.

Highest-signal resume keywords

Apache SparkData Pipeline DevelopmentCloud-Native Data PlatformsMedallion ArchitectureDbt

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Spark (Scala, Python)SQLKafkaData ModelingPerformance TuningData QualityObservabilityCI/CDInfrastructure AutomationDistributed Data Processing

Soft Skills

CommunicationStakeholder ManagementCollaboration

Tools & Technologies

Amazon S3BigQueryTrinoMetabaseMaxwell

Industry Keywords

Data WarehousingData LakesEvent-Driven ArchitecturesData GovernanceData Mart

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 data models following Medallion Architecture (Bronze, Silver, Gold)
Build and maintain cloud-native data platforms using S3, Spark, Trino, and BigQuery
Design data ingestion frameworks leveraging CDC (Maxwell), Kafka, and event-driven architectures
Create, optimize, and maintain data warehouses and data marts
Partner with Product Managers, Data Analysts, Backend Engineers, and Business stakeholders to translate requirements into data solutions
Develop reusable dbt models, testing frameworks, and documentation
Optimize Spark jobs, Trino queries, and storage layouts
Own the lifecycle of critical data pipelines, including availability, monitoring, SLA adherence, and incident resolution
Build reusable frameworks, automation, CI/CD pipelines, and engineering best practices for the core data platform
Ensure data quality through validation, monitoring, lineage, and observability, while implementing security and governance practices
Deliver trusted datasets, semantic models, and Metabase dashboards for analytics and 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, including reusable models, automated testing, and documentation
Strong 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
Strong communication and stakeholder management skills
Experience collaborating across Product, Engineering, Analytics, and Business teams
Passion for scalable data platforms, developer experience, platform reliability, and operational excellence

Benefits

Comp & perks

Inclusive and Diverse Environment: We foster an inclusive and diverse workplace that values innovation and offers remote environments.
Competitive compensation packages
Potential share options for certain roles
Regular training
Annual learning stipend
High degree of autonomy
Mentorship
Ambitious goals supporting professional and company growth