Data Engineer

About the role

We are looking for an experienced Data Engineer to design, build, and maintain scalable data pipelines that support growing data volumes and complexity. The role works closely with analytics, business teams, and the Data Platform group to ensure data quality, accessibility, and performance across the organization.

Key responsibilities

• Develop and maintain robust ETL workflows, scripts, and custom integrations using Python, Spark, Hive, and related technologies.
• Collaborate with analytics and business stakeholders to improve data models and support BI tools.
• Implement monitoring, validation, and error‑handling processes to guarantee data quality and availability.
• Raise change requests for production deployment and ensure compliance with SLAs.
• Optimize SQL queries, ETL jobs, and platform resources; recommend hardware or architectural upgrades as needed.
• Document data models, pipelines, dictionaries, and standard operating procedures.
• Provide support to data analysts, BI teams, and other end‑users.
• Work with incident management to track and resolve data‑related issues.

Required profile

• Minimum 5 years of professional experience in data engineering.
• Strong background in the banking or finance sector, understanding of related processes and regulations.
• Proven ability to lead or mentor a small technical team.
• Experience with Azure cloud services, especially Azure Data Factory, Databricks, and cloud storage.

Required skills

• Hadoop ecosystem (HDFS, Hive, Cloudera)
• Apache Spark
• Python programming
• SQL (relational) and NoSQL databases
• Azure Data Factory, Azure Databricks, Azure Storage
• Airflow workflow orchestration
• Kafka data streaming
• CI/CD pipelines