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Autofleet

Senior Data Engineer

Autofleet

. Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark and other modern tools .

Posted 9/23/2026full-timeBengaluru • IndiaSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and maintaining scalable data pipelines using Spark and Google Cloud Platform, with a strong focus on data quality and performance. Proficient in Python and SQL, with experience in ETL/ELT workflows and orchestration tools.

Highest-signal resume keywords
Data Pipeline DesignPython ProgrammingSQL ProficiencyGoogle Cloud PlatformETL/ELT Workflow Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data EngineeringBackend DevelopmentData ModelingDistributed Data ProcessingData Quality Monitoring
Soft Skills
CollaborationCommunication
Tools & Technologies
SparkAirflowDockerKubernetesSnowflake
Industry Keywords
Cloud-Native EnvironmentsData LakesData PipelinesCI/CD PracticesMachine Learning Workflows

Tech Stack

Tools & technologies
AirflowAWSCloudDockerETLGoogle Cloud PlatformKafkaKubernetesPythonSparkSQL

About the role

Key responsibilities & impact
  • Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark and other modern tools
  • Own backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets
  • Leverage Google Cloud Platform services to architect and operate scalable, secure, and cost-effective data solutions
  • Develop and optimize ETL/ELT workflows across multiple environments for internal applications, analytics, and machine learning workflows
  • Build and maintain data marts and data models focused on performance, data quality, and long-term maintainability
  • Collaborate with development teams, product managers, and external stakeholders to translate data requirements into scalable solutions
  • Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability

Requirements

What you’ll need
  • 4+ years in backend data engineering or infrastructure-focused software development
  • Proficient in Python, with experience building production-grade data services
  • Solid understanding of SQL
  • Proven track record designing and operating scalable, low-latency data pipelines (batch and streaming)
  • Experience building and maintaining data platforms, including lakes, pipelines, and developer tooling
  • Familiar with orchestration tools like Airflow, and modern CI/CD practices
  • Comfortable working in cloud-native environments (AWS, GCP), including containerization (e.g., Docker, Kubernetes)
  • Bonus: Experience working with GCP
  • Bonus: Experience with data quality monitoring and alerting
  • Bonus: Experience with Snowflake, DBT, Flink, Kafka
  • Bonus: Strong hands-on experience with Spark for distributed data processing at scale
  • Degree in Computer Science, Engineering, or related field

Benefits

Comp & perks
  • No benefits or compensation extras specified