DATABRICKS Data Engineer

Greetings from TCS Recruitment Team! *Face to Face* For all those

DATABRICKS Data Engineer we are coming bigger with the plan of

Face to Face Drive on 11th July,2026 (Saturday) in Vishakhapatnam. It is a Face to Face interview planned to attract great Talents in

DATABRICKS Data Engineer We believe that your skills and expertise are a better match for the skills we are looking for. Skill:

DATABRICKS Data Engineer (Face to Face) Years of experience:

5 to 12 Years Location

: Vishakhapatnam Date:

11th July,2026 (Saturday) (Face to Face) Drive Time:

9AM to 2 PM Total IT Experience (in Yrs.) 5-12yrs

Relevant Experience Required (in Yrs.)

• Hands-on experience in

developing, maintaining, and optimizing data pipelines using

Databricks .

• Knowledge of

ETL/ELT pipelines, data ingestion, transformation, and orchestration .

• Exposure to

data lake, data warehouse, or lakehouse architectures .

• Experience with

data migration, integration, and automation .

• Collaboration with senior engineers, architects, and analytics teams for solution delivery.
• Familiarity with

security, access control, and compliance best practices.

• Working experience in

Agile and DevOps environments .

Language Requirement :

English Key words to search in resume Databricks Data Engineer, Spark, PySpark, Delta Lake, ETL, ELT, Data Pipeline, Python, SQL, Data Lake, Lakehouse, Data Warehouse, AWS, Azure, GCP, Airflow, CI/CD, Data Integration, Stream Processing, Batch Processing

Technical/Functional Skills -MUST HAVE SKILLS

• Hands-on experience with

Databricks platform , including:

• PySpark / Spark SQL for data processing and transformation
• Delta Lake for ACID-compliant data storage
• Notebooks for workflow orchestration and collaborative development
• Strong programming skills in

Python and

SQL .

• Experience in

data ingestion from batch and streaming sources.

• Knowledge of

ETL/ELT design patterns and data pipeline optimization.

• Familiarity with

cloud data storage and compute environments (AWS, Azure, or GCP).

• Basic understanding of

workflow orchestration tools (Airflow, Databricks Jobs).

• Exposure to

DevOps concepts , CI/CD pipelines, and version control (Git).

• Awareness of

data security , access control, and compliance considerations.

• Understanding of

performance tuning , partitioning, and caching strategies in Spark.

Secondary Skills

• Knowledge of

BI/analytics tools such as Power BI, Tableau, or Looker.

• Familiarity with

containerization (Docker, Kubernetes) for workload deployment.

• Basic knowledge of

machine learning pipelines in Databricks (MLflow, Spark MLlib).

• Understanding of

Agile methodologies and collaborative development practices.

• Exposure to

multi-cloud data integration is a plus.

Responsibilities

• Develop and maintain

scalable data pipelines using Databricks (PySpark, Delta Lake).

• Ingest, process, and transform structured and unstructured data from multiple sources.
• Collaborate with senior engineers and architects to implement

data lakehouse or warehouse solutions .

• Optimize pipelines for

performance, reliability, and cost efficiency .

• Implement

ETL/ELT workflows for batch and streaming data processing.

• Ensure

data quality, validation, and error handling in pipelines.

• Support

DevOps integration , CI/CD pipelines, and automated deployments.

• Document data processes, workflows, and pipeline configurations.
• Troubleshoot and resolve pipeline or processing issues.
• Work closely with analytics, BI, and data science teams to deliver

actionable insights .

• Learn and apply

new Databricks features and emerging data engineering best practices.