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Autofleet

Senior Data Engineer

Autofleet

Senior Data Engineer managing data pipelines and advanced analytics for mobility solutions. Collaborating with teams to optimize operations and harness machine learning applications.

Posted 7/13/2026full-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 and backend data infrastructure, leveraging Google Cloud Platform services for optimal data solutions. Proficient in ETL/ELT workflows and collaboration with cross-functional teams to translate data requirements into effective solutions.

Highest-signal resume keywords
Data Pipeline DesignGoogle Cloud Platform (GCP)ETL/ELT WorkflowsSparkData Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data PipelineBatch ProcessingReal-Time ProcessingData TransformationData ValidationData OrchestrationMachine LearningOptimization AlgorithmsAdvanced AnalyticsData Quality
Soft Skills
CollaborationCommunication
Tools & Technologies
SparkGoogle Cloud Platform (GCP)
Industry Keywords
Data InfrastructureDistributed Data ProcessingData MartData ModelScalability

Tech Stack

Tools & technologies
CloudETLGoogle Cloud PlatformSpark

About the role

Key responsibilities & impact
  • We are making the future of Mobility come to life starting today.
  • At Autofleet we support the world’s largest vehicle fleet operators and transportation providers to optimize existing operations and seamlessly launch new, dynamic business models - driving efficient operations and maximizing utilization.
  • At the heart of our platform lies the data infrastructure, driving advanced machine learning models and optimization algorithms. As the owner of data pipelines, you'll tackle diverse challenges spanning optimization, prediction, modeling, inference, transportation, and mapping.
  • As a Senior Data Engineer, you will play a key role in owning and scaling the backend data infrastructure that powers our platform—supporting real-time optimization, advanced analytics, and machine learning applications.

Requirements

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

Benefits

Comp & perks
  • 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.