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Data Engineer (Databricks Specialist)_Virtual drive

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Onsite

Data Engineer (Databricks Specialist)_Virtual drive

Tata Consultancy Services

Data Engineer (Databricks Specialist)_Virtual drive

Skills

Google Cloud PlatformSQL QueriesBig DataData EngineeringSnowflakeDatabricksData AnalysisMachine LearningAWSPySparkTalendPower BIAzure

2 months ago

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Important information


Contract type:

Permanent

Salary:

₹600,000 – ₹1,200,000 / year

Location:

Hyderabad, Telangana, India

Work mode:

Onsite

Published on:

31 August 2026

What they need


Data Engineer (Databricks Specialist)

Exp: 6 to 12 years

Location: Hyderabad Only

Interview Mode: Virtual interview in Week days

Role & responsibilities

Desired Competencies (Technical/Behavioral Competency)

Must-Have**

(Ideally should not be more than 3-5)

  • Databricks & Spark: Hands-on experience with Databricks, Apache Spark, and PySpark for big data processing.
  • Cloud Platforms: Proficiency in Azure (preferred), AWS, or GCP for data engineering workflows
  • SQL & Data Modeling: Strong knowledge of SQL, schema design, and dimensional data modeling.
  • ETL/ELT Tools: Experience with tools like Azure Data Factory, Informatica, or Talend.
  • Data Warehousing: Familiarity with technologies such as Azure Synapse, Snowflake, or BigQuery.
  • Strong problem-solving and debugging skills.
  • Excellent communication and teamwork abilities.
  • Ability to work in a fast-paced, dynamic environment.

Good-to-Have

  • Knowledge of Event Hub, IoT Hub, and streaming analytics.
  • Exposure to Power BI, Azure DevOps, and CI/CD pipelines.
  • Understanding of machine learning libraries (MLlib) and predictive analytics.

SN

Responsibility of / Expectations from the Role

1Design and maintain scalable data pipelines using Databricks and PySpark.

2Implement and optimize ETL processes for large datasets.

3Collaborate with cross-functional teams to define data requirements.

4Ensure data integrity and quality across the lifecycle.

5Integrate solutions with cloud platforms and manage large-scale datasets.

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