Reference: Vrg2425091
Job title: Data Engineer
Role Overview
We are seeking an exceptional Data Engineer to design, develop, and scale data pipelines, warehouses, and streaming systems that power mission-critical analytics and AI workloads. This is a hands-on engineering role where you will work with cutting-edge technologies, tackle complex data challenges, and shape the organization’s data architecture.

Core Responsibilities

• Design & own robust ETL/ELT pipelines using Python and Apache Spark for batch and near real-time processing.
• Architect and optimize enterprise data warehouses (Snowflake, BigQuery, Redshift, Azure Synapse) for performance and scalability.
• Build data models and implement governance frameworks ensuring data quality, lineage, and compliance.
• Engineer streaming data solutions using Kafka and Kinesis for real-time insights.
• Collaborate cross-functionally to translate business needs into high-quality datasets and APIs.
• Proactively monitor, tune, and troubleshoot pipelines for maximum reliability and cost efficiency.

Must-Have Expertise

• 5–9+ years in data engineering, with proven delivery of enterprise-scale solutions.
• Advanced skills in Python, Apache Spark, and SQL optimization.
• Deep expertise in at least one leading data warehouse (Snowflake, BigQuery, Redshift, Azure Synapse).
• Strong knowledge of data modeling, governance, and compliance best practices.
• Hands-on experience with streaming data technologies (Kafka, Kinesis).
• Cloud proficiency in AWS, Azure, or GCP.

Preferred Qualifications

• Experience with Airflow, dbt, or similar orchestration frameworks.
• Exposure to DevOps & CI/CD in data environments.
• Familiarity with ML pipelines and feature stores.

Job Types: Full-time, Fixed term contract
Application deadline: 10/09/2025
Reference ID: Vrg242509

• Design & own robust ETL/ELT pipelines using Python and Apache Spark for batch and near real-time processing.
• Architect and optimize enterprise data warehouses (Snowflake, BigQuery, Redshift, Azure Synapse) for performance and scalability.
• Build data models and implement governance frameworks ensuring data quality, lineage, and compliance.
• Engineer streaming data solutions using Kafka and Kinesis for real-time insights.
• Collaborate cross-functionally to translate business needs into high-quality datasets and APIs.
• Proactively monitor, tune, and troubleshoot pipelines for maximum reliability and cost efficiency