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Data Solution Engineer

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Hybrid

Data Solution Engineer

Robosoft Technologies

Data Solution Engineer

Skills

Google Cloud PlatformSQL QueriesBig DataPythonKafkaRedisMicrosoft Azure Cosmos DBDatabricksData AnalysisMachine LearningAIScalaGitHubKubernetesWeb AnalyticsGoogle AnalyticsPySparkApache AirflowAdobe AnalyticsTerraformAzure

12 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

7 October 2026

What they need


  • Core experience: Minimum 5 years of hands-on experience in data processing pipeline and API development, with strong proficiency in Python (PySpark) and Scala a must
  • Big data & distributed systems: Apache Spark, Databricks, Blob Storage, BigQuery and managed Spark (Dataproc) on GCP; processing structured, semi-structured and multi-modal data (text, image, audio, video)
  • Lakehouse & table formats: Delta Lake, Apache Iceberg and Apache Hudi
  • Data modelling & stores: Designing data schemas, tables and partitioning strategies across SQL and NoSQL databases such as Cosmos DB and Redis
  • Pipelines & orchestration: Building batch and streaming ETL/ELT processes over pub/sub platforms (Kafka, Azure Event Hubs, Google Cloud Pub/Sub), orchestrated with Airflow on Kubernetes, in containerized, scalable and serverless architectures
  • Infrastructure as Code: Terraform or equivalent
  • Performance & cost optimization: Spark tuning, shuffle optimization, caching, cluster sizing, query optimization, debugging long-running jobs and cloud cost control
  • Data quality & lineage: Defining data quality rules and data lineage definitions, and creating monitoring alerts out of them
  • Testing for data pipelines: Data validation, regression tests, reconciliation and end-to-end pipeline testing
  • Observability & reliability: Centralized logging, metrics, tracing, job monitoring, SLAs/SLOs and incident troubleshooting
  • ML engineering: Feature store, feature and training pipelines, model deployment and serving, model monitoring with drift detection and retraining (MLflow / MLOps)
  • AI-assisted development: Hands-on experience using tools such as GitHub Copilot, Claude Code or equivalent for code generation, debugging, testing and developer productivity
  • Nice to have: Past exposure to web analytics solutions (Adobe Analytics & Google Analytics)

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