Data Engineer

Role Overview

We are looking for a Data Engineer to help build and support modern cloud-based data solutions. The role involves working with large datasets, developing reliable data pipelines, and enabling analytics teams with high-quality, ready-to-use data.

Responsibilities

• Build scalable data pipelines and ingestion workflows using GCP, Azure, or AWS and Databricks.
• Develop data processing and transformation solutions with Python, SQL, and Spark/PySpark.
• Work extensively with BigQuery and Google Cloud Storage.
• Use Databricks Notebooks and Workflows to develop and manage data engineering workloads.
• Support both scheduled/batch processing and near real-time data ingestion.
• Ensure data pipelines are reliable through monitoring, validation, and quality checks.
• Prepare and maintain datasets used by BI, reporting, and analytics teams.
• Work with engineers and architects to improve data platforms and engineering practices.

Requirements

• 2+ years of hands-on Data Engineering experience.
• Strong working knowledge of at least one major cloud platform (GCP, Azure, or AWS), with hands-on experience in Databricks, BigQuery, and/or cloud storage technologies.
• Good command of Python and SQL.
• Experience with ETL/ELT development and data warehousing concepts.
• Understanding of structured and unstructured data processing.
• Familiarity with Airflow or Cloud Composer.
• Knowledge of Git, CI/CD, and Agile development.

Nice to Have

• Exposure to Azure, particularly Azure Data Factory (ADF).
• Exposure to Kafka or Google Pub/Sub and streaming concepts.

Skills

• Python
• SQL
• Databricks
• GCP
• PySpark