Remote Data Engineer: Scalable Pipelines & Lakehouse

Virtusa Corporation; United States; Remoto; USD 120.000 - 160.000; Tempo pieno

Descrizione del lavoro

Virtusa Corporation seeks two Data Engineers for a fully remote role to design, build, and maintain scalable data pipelines and lakehouse infrastructure powering analytics, ML, and AI workflows.

You will work with Databricks, Snowflake, AWS or Azure, write SQL and Python, and use dbt, Airflow, and Databricks Workflows to ensure reliable, governed data.

Onboarding may align with US hours; a strong focus on data quality, performance, and observability is essential.

Competenze

• 3+ years of hands-on experience as a Data Engineer building production data pipelines.
• Proficiency in SQL and Python with distributed processing (Databricks PySpark).
• Experience with modern data platforms (Databricks, Snowflake, AWS or Azure) and orchestration tools (dbt, Airflow, or Databricks Workflows).
• Solid understanding of data modeling and CI/CD best practices (Git).
• Strong focus on data reliability, performance, and monitoring.

Mansioni

• Design, build, and maintain scalable batch and streaming ETL/ELT pipelines in cloud environments (Databricks / Snowflake / AWS / Azure).
• Transform raw data into clean analytical models using SQL, Python, and dbt.
• Implement automated data quality checks, schema validation, logging, and pipeline observability.
• Collaborate with Ontology, Knowledge Graph, and Data Science teams to prepare datasets for graph ingestion and ML feature stores.
• Enforce data governance, lineage, and security standards (RBAC, PII handling, GDPR).

Conoscenze

SQL

Python

Databricks

Snowflake

Airflow

dbt

Data Modeling

Git

Databricks Workflows

Strumenti

Databricks Workflows

Descrizione del lavoro

Virtusa Corporation seeks two Data Engineers for a fully remote role to design, build, and maintain scalable data pipelines and lakehouse infrastructure powering analytics, ML, and AI workflows.

You will work with Databricks, Snowflake, AWS or Azure, write SQL and Python, and use dbt, Airflow, and Databricks Workflows to ensure reliable, governed data.

Onboarding may align with US hours; a strong focus on data quality, performance, and observability is essential.