Data Engineer

Virtusa Corporation; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

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.

Qualifications

• 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.

Responsibilities

• 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).

Skills

SQL

Python

Databricks

Snowflake

Airflow

dbt

Data Modeling

Git

Databricks Workflows

Tools

Databricks Workflows

Job description

This is a remote position.

We are looking for two Data Engineers to build and maintain the scalable pipelines and lakehouse infrastructure that power our analytics, machine learning, and AI agent platforms. You will ensure that clean, reliable, and well-governed data flows seamlessly from raw operational sources into analytical layers, knowledge graphs, and AI workflows.

Key Responsibilities:

• Design, build, and maintain scalable batch and streaming ETL/ELT pipelines in cloud environments (Databricks / Snowflake / AWS / Azure).
• Transform raw structured and semi-structured data into clean, curated analytical models (medallion architecture: Bronze/Silver/Gold) using SQL, Python, and dbt.
• Implement automated data quality checks, schema validation, logging, and pipeline observability.
• Collaborate closely with Ontology, Knowledge Graph, and Data Science teams to prepare reliable datasets for downstream graph ingestion and ML feature stores.
• Enforce data governance, lineage, and security standards (role-based access control, PII handling, GDPR compliance).

Work Schedule: During the onboarding phase, working hours may be adjusted to overlap with US hours until 7:00 PM at the latest.

Requirements

• 3+ years of hands-on experience as a Data Engineer building production data pipelines.
• Strong proficiency in SQL and Python, with practical experience in distributed data processing (Databricks and PySpark).
• Hands-on experience with modern data platforms (Databricks, Snowflake, AWS, or Azure) and transformation/orchestration tools (dbt, Apache Airflow, or Databricks Workflows).
• Solid understanding of data modeling concepts (dimensional modeling, lakehouse architecture) and CI/CD best practices (Git).
• Detail-oriented approach to data reliability, performance tuning, and pipeline monitoring.