Remote Senior GCP Data Engineer: BigQuery & Dataflow

Danta Technologies; Secaucus (NJ); Remote; USD 51,000 - 73,000; Full time

Job summary

Danta Technologies is seeking a Senior GCP Data Engineer in Secaucus, NJ (Remote). You will design and maintain scalable data pipelines on GCP, focusing on ingestion, transformation, and governance to support enterprise analytics.

The role requires expert Python, SQL, and BigQuery skills, and experience migrating data from on-premise warehouses. You will collaborate with architects, analysts, and stakeholders to build reusable data models, optimize costs, and ensure data quality and security

Qualifications

• Experience designing scalable data pipelines in GCP.
• Strong SQL and Python for data engineering.
• Experience migrating data from on-premise to BigQuery.
• Familiarity with metadata, lineage, and data governance.
• Excellent communication in Agile teams.

Responsibilities

• Design end-to-end data pipelines with Dataflow for batch and streaming.
• Develop BigQuery SPs, SQL scripts, and UDFs.
• Build data workflows with Cloud Composer/Airflow.
• Ingest data from on-prem and cloud sources to GCS and BigQuery.
• Migrate history data to BigQuery from Snowflake/Teradata/Netezza/Oracle/SQL Server.
• Implement data validation, quality testing, and governance.
• Optimize costs and performance of GCP workloads.
• Collaborate with analysts and architects on data models and semantic layers.

Skills

GCP Expertise

BigQuery

Dataflow

Cloud Composer

GCS

Python

SQL

Data modeling

Data governance

Airflow

Tools

Snowflake

Teradata

Netezza

Oracle

SQL Server

Job description

Danta Technologies is seeking a Senior GCP Data Engineer in Secaucus, NJ (Remote). You will design and maintain scalable data pipelines on GCP, focusing on ingestion, transformation, and governance to support enterprise analytics.

The role requires expert Python, SQL, and BigQuery skills, and experience migrating data from on-premise warehouses. You will collaborate with architects, analysts, and stakeholders to build reusable data models, optimize costs, and ensure data quality and security