data engineer in risk and compliance

Solvd Inc. is an AI-native consulting and technology services firm delivering enterprise transformation across cloud, data, software engineering, and artificial intelligence. Following the acquisition of Tooploox, the company provides end-to-end delivery from strategic advisory and solution design to custom AI development and enterprise-scale implementation.

• Design, build, and optimize scalable data infrastructure for risk and compliance workloads;
• Develop batch and streaming pipelines using modern big data technologies;
• Implement robust ETL and ELT workflows across structured and unstructured data sources;
• Work extensively with AWS data services, including EMR, Redshift, S3, Glue, Lambda, and Kinesis;
• Build high-throughput, low-latency systems for real-time or near-real-time decision-making;
• Partner with data scientists, analysts, and engineering teams to deliver reliable and well-documented datasets;
• Translate business requirements into technical specifications and scalable data solutions;
• Mentor team members and contribute to best practices and reusable frameworks;
• Streamline reporting, analytics, and data preparation processes;
• Replace manual workflows with automated, repeatable systems;
• Support performance tuning, monitoring, and platform optimization.

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a similar field;
• 3 Or more years of hands-on experience in data engineering or related fields;
• Strong proficiency in SQL and data modeling for analytics and warehousing;
• Experience building ETL or ELT pipelines at scale;
• Experience with Hadoop, Hive, Spark, HBase, or EMR;
• Knowledge of distributed systems and data storage principles;
• Basic scripting skills in Python or Scala;
• Familiarity with machine learning concepts;
• Nice to have: Experience with AWS data services such as Redshift, S3, Glue, EMR, Kinesis, Firehose, and Lambda; experience with non-relational data stores; experience collaborating with cross-functional risk, compliance, or analytics teams; strong understanding of data governance, quality, and security best practices.

• Global team with collaboration opportunities across continents and cultures;
• Inclusive environment focused on continuous learning, innovation, and ethical AI standards.