Requirements

Expected technologies

SQL

PL/SQL

Python

Linux

Bash

Apache Spark

Kafka

Optional technologies

Scala

Google Cloud Platform

Our requirements

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Economics, or a related field.
• Minimum 3 years of experience as a Data Analyst or Data Quality Analyst in a data-driven organization.
• Proven experience in data quality management and data governance practices.
• Strong database expertise, including advanced SQL and PL/SQL.
• Proficiency in Python, with experience in Scala as a plus.
• Proficiency in cloud platforms and services (preferably GCP).
• Experience with Linux and bash scripting.
• Solid understanding of Cloudera Hadoop technology stack (Apache Spark, Apache Kafka).
• Knowledge of data handling principles, including ETL processes and real-time data processing.

Optional

• Experience with CI/CD pipelines and automation tools.
• Strong understanding of data governance principles, including metadata management and data quality frameworks.
• Ability to work with and understand various source system types (Kafka, MQ, SFTP, databases, APIs, file shares).
• Experience in international or multi-country data projects is preferred.
• Strong communication skills to translate technical findings into business-oriented insights.
• Fluent in English, both written and spoken.
• Self-starter with a continuous learning mindset and strong attention to detail.

Your responsibilities

• Build and maintain data ingestion processes from various sources into the Data Lake.
• Design, develop, and optimize complex data pipelines for reliable data flow.
• Build, develop, and maintain frameworks that facilitate the construction of data pipelines.
• Implement end-to-end testing frameworks for data pipelines.
• Collaborate with data analysts and scientists to ensure the delivery of quality data.
• Ensure robust data governance, security, and compliance practices.
• Explore and implement emerging technologies to improve data pipeline performance.
• Utilize and integrate data from various source system types, including Kafka, MQ, SFTP, databases, APIs, and file shares