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Data Engineer

Virtusa

Role overview

Summary

This role focuses on building and maintaining scalable data pipelines and lakehouse infrastructure to support analytics, machine learning, and AI platforms. You will work with various teams to ensure clean and reliable data flows into analytical layers and workflows.

Qualifications

  • 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.
  • Hands-on experience with modern data platforms and transformation/orchestration tools.
  • Solid understanding of data modeling concepts and CI/CD best practices.
  • Detail-oriented approach to data reliability, performance tuning, and pipeline monitoring.

Responsibilities

  • Design, build, and maintain scalable batch and streaming ETL/ELT pipelines in cloud environments.
  • Transform raw structured and semi-structured data into clean, curated analytical models 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.

Key facts

Hard skills

Other skills

  • Detail Oriented
  • Collaboration

About the company

Virtusa logo

Virtusa

  • IT Services & Consulting
  • 10,001+
Virtusa Corporation is a global provider of Digital Business Transformation, Digital Engineering, and Information Technology (IT) outsourcing services that accelerate our clients’ journey to their Digital Future. Virtusa serves Global 2000 companies in Banking, Financial Services, Insurance, Healthcare, Telecommunications, Media, Entertainment, Travel, Manufacturing, and Technology industries. Using a combination of digital strategy, digital engineering, business implementation, and IT platform modernization services, Virtusa helps clients execute successful end-to-end digital business transformation initiatives. Virtusa engages its clients to re-imagine their business models and develop strategies to defend and grow their business by introducing innovative products and services, developing distinctive digital consumer experiences, creating operational efficiency using digital labor, developing operational and IT platforms for the future, and rationalizing and modernizing their existing IT applications infrastructure. As a result, its clients are simultaneously able to drive business growth through digital-first customer experiences, while also consolidating and modernizing their IT application infrastructure to support digital business transformation. Holding a proven record of success across industries, Virtusa readily understands its clients' business challenges and uses its domain expertise to deliver innovative applications of technology to address its clients’ critical business challenges. Examples include building the world's largest property & casualty claims modernization program; one of the largest corporate customer portals for a premier global bank; an order to cash implementation for a multinational telecommunications provider; and digital transformation initiatives for media and banking companies. Founded in 1996 and headquartered in Massachusetts, Virtusa has operations in North America, Europe, and Asia.
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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.



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