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Data Engineer

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Remote

Data Engineer

Virtues R & D

Data Engineer

Skills

SQL QueriesDockerPythonJavaKafkaGitJenkinsDatadogData AnalysisAIScalaAWSdbtGitHubKubernetesAzure

2 days ago

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Important information


Contract type:

Permanent

Location:

Hyderabad, Telangana, India

Work mode:

Remote

Published on:

21 October 2026

What they need


Data Engineer: Microsoft Fabric, Batch and Streaming Pipelines

Experience: 5+ years (adjust to the client's level) | Hours: Full-time, 40 hrs/week, aligned to client's team time zone

Role Overview
Join a product-focused team to build and run the data pipelines behind customer-facing applications and analytics. You'll own your pipelines from first design through production support, with a focus on Microsoft Fabric.

Key Responsibilities

  • Develop robust batch and streaming pipelines that pull data from APIs, databases, event streams, and external vendor systems.
  • Create reusable patterns for ingesting, transforming, and storing data, and model it into consistent, trusted datasets.
  • Build and troubleshoot workflows in Microsoft Fabric Notebooks and move them into production.
  • Put automated testing, version control, and CI/CD around every pipeline you ship.
  • Set up data quality checks, alerting, and monitoring so issues surface before users notice them.
  • Tune queries, pipelines, and storage to keep runtime and cloud spend under control.
  • Maintain clear documentation of data flows, lineage, and metadata.
  • Work within agile ceremonies (planning, standups, retros) alongside product managers, developers, and analysts.
  • Follow data privacy and security standards, and help mentor junior engineers.

Mandatory Skills

  • Programming: Python, Java, or Scala for pipeline development
  • Data Frameworks: Hands-on with at least two of Spark, Kafka, Flink, dbt
  • Databases: Strong SQL plus working knowledge of NoSQL; solid data modeling fundamentals
  • Platform: Microsoft Fabric Notebooks, used in real project work
  • Containers & Orchestration: Docker and Kubernetes
  • Monitoring: Experience with observability tooling; Datadog strongly preferred
  • Performance: Proven SQL and pipeline optimization for speed and cost
  • Good to have: Fabric Lakehouse, Warehouse, Data Pipelines, Dataflows Gen2, Semantic Models; Azure or AWS data services; CI/CD tools (GitHub Actions, Jenkins, Azure DevOps); use of generative or agentic AI tools to speed up engineering work

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