Digital : Python(MongoDB, Python, Pyspark, Big Query, GCS)

Weekday virtual drive

7+ years

21-Aug-26

12-2pm

Hyderabad

We are pleased to invite you for an interview scheduled on

Interview Details:• Date:

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21-Aug-2

12-2pm

Hyderabd

Please share updated resume

Name:

Contact Number:

Email ID:

Highest Qualification in: (Eg. B.Tech/B.E./M.Tech/MCA/M.Sc./MS/BCA/B.Sc./Etc.)

Current Organization Name:

Total IT Experience-7 to 10 yrs

LOCATION TCS Hyderabab

Current CTC

Expected CTC

Notice period:

Whether worked with TCS - Y/N

Please apply only if your skill matches

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Digital : Python(MongoDB, Python, Pyspark, Big Query, GCS)

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1

Role**

Mongo db GCP Data Engineer Python Pyspark Developer (BigQuery, Cloud Storage, Dataproc, Airflow)

2

Required Technical Skill Set**

GCP Data Engineer to design, build, and optimize scalable data pipelines and analytics solutions using BigQuery, Cloud Storage, Dataproc, and Airflow.

Desired Experience Range**

7+ Years

Location of Requirement

HYDERABAD

Immediate Joiners Needed

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Desired Competencies (Technical/Behavioral Competency)

Must-Have**

(Ideally should not be more than 3-5)

• GCP Services: BigQuery, Cloud Storage, Dataproc, Cloud Composer (managed Airflow) or self-managed Airflow.
• Airflow: Strong experience in DAG creation, operators/hooks, scheduling, backfilling, retry strategies, and CI/CD for DAG deployments.
• Programming: Proficiency in Python and Pyspark (PySpark, Airflow DAGs), SQL (advanced BigQuery SQL).
• Data Modeling: Dimensional modeling (Star/Snowflake), data vault basics, and schema design for analytics.
• Performance Tuning: BigQuery partitioning/clustering, predicate pushdown, job stats review, Dataproc executor tuning.
• Version Control & CI/CD: Git, branching strategies, pipelines for deploying Airflow DAGs and config.
• Operational Excellence: Monitoring with Stackdriver/Cloud Logging, debugging pipeline failures, and root-cause analysis.
• involves end-to-end ownership of data ingestion, transformation, orchestration, and performance tuning for batch and near real-time workflows.

Good-to-Have

• Streaming: Pub/Sub, Dataflow (Apache Beam) for near real-time pipelines.
• Orchestration Patterns: Event-driven pipelines, dependency management, and cross-environment promotion.
• Data Governance: Catalog/lineage tools (e.g., Data Catalog), PII handling, row-level security, column-level encryption.
• Containers & Infra: Docker, Terraform for IaC on GCP; Kubernetes concepts.
• BI Integration: Experience integrating with Looker, Tableau, or Power BI.
• Certifications: Google Professional Data Engineer / Cloud Architect.