Data Engineer

A Leading Financial Data Services firm is seeking a highly skilled Data Engineer to join its growing data platform team. In this role, you will be responsible for building and maintaining robust data pipelines that power our advanced financial analytics products. Furthermore, you will work with large-scale datasets to ensure data quality, reliability, and accessibility for various business stakeholders. Consequently, this position is vital for driving data-led insights across the financial services landscape.

The Data Engineer must possess a deep understanding of Big Data technologies and cloud-based data architecture. Additionally, you will lead the implementation of ETL/ELT processes and optimize database performance for high-concurrency workloads. Therefore, the firm is looking for a professional with a passion for building scalable data infrastructure. If you are an expert in SQL, Python, and modern data stack tools, this Data Engineer role offers a premier technical platform.

Key Responsibilities

• Design, build, and maintain scalable data pipelines using Python, Scala, or Java.
• Develop robust ETL/ELT workflows to ingest and transform data from diverse financial sources.
• Optimize data storage and retrieval using Big Data technologies like Spark, Hadoop, or Snowflake.
• Collaborate with data scientists and analysts to provide clean, structured data for modeling.
• Implement data quality monitoring and automated testing to ensure high data integrity.
• Design and maintain cloud-based data warehouses (AWS Redshift, BigQuery, or Azure Synapse).
• Troubleshoot and resolve complex data performance bottlenecks in production environments.
• Participate in architectural reviews and contribute to the long-term data strategy.
• Ensure data security and compliance with relevant financial data privacy regulations.
• Mentor junior data engineers and advocate for engineering best practices in data management.

Requirements and Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related quantitative field.
• 3–6 years of experience in data engineering or a related backend engineering role.
• Strong proficiency in SQL and Python; experience with Big Data frameworks (Spark/Kafka) is a plus.
• Deep understanding of data modeling, data warehousing, and distributed systems.
• Excellent problem-solving skills and the ability to work in a fast-paced environment.