Data Engineer

Virtusa

. Design, build, and maintain scalable batch and streaming ETL/ELT pipelines in cloud environments .

Posted 11/12/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining scalable ETL/ELT pipelines in cloud environments, utilizing SQL, Python, and modern data platforms. Strong focus on data governance, quality assurance, and collaboration with cross-functional teams to support analytics and machine learning initiatives.

Highest-signal resume keywords

Data Pipeline DevelopmentSQL ProficiencyPython ProgrammingDatabricks ExperienceData Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ETL/ELT Pipeline DesignSQLPythonDatabricksPySparkDbtApache AirflowData ModelingCI/CD Best PracticesPerformance Tuning

Soft Skills

Detail-OrientedCollaborative

Tools & Technologies

Databricks WorkflowsSnowflakeAWSAzure

Industry Keywords

Data GovernanceGDPR ComplianceRole-Based Access ControlLakehouse ArchitectureDimensional Modeling

Tech Stack

Tools & technologies

AirflowApacheAWSAzureCloudETLPySparkPythonSnowflakeSQL

About the role

Key responsibilities & impact

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 medallion architecture (Bronze/Silver/Gold), 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, including role-based access control, PII handling, and GDPR compliance
Build and maintain scalable pipelines and lakehouse infrastructure supporting analytics, machine learning, and AI agent platforms
Ensure reliable data flows from operational sources into analytical layers, knowledge graphs, and AI workflows
Adjust working hours during onboarding to overlap with US hours, up to 7:00 PM at the latest

Requirements

What you’ll need

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

Benefits

Comp & perks

Remote work arrangement
Full-time employment