Data Solutions Architect (Financial Services)

Role overview A senior, client-facing Solutions Architect role split evenly between hands-on technical delivery and presales support, focused on a leading lakehouse and data AI platform. The work spans financial-services customers, combining end-to-end data engineering, solution design, and architecture leadership to ship production-grade analytics and ML systems.

Responsibilities - Act as a trusted advisor to customers, shaping data engineering and architecture strategy around modern lakehouse and ML platforms. - Design, build, and deploy end-to-end data solutions covering ingestion, transformation, and activation for BI, ML, and AI use cases. - Partner with sales teams in presales motions: lead discovery, run solution design sessions, and present technical value to prospective clients. - Architect solutions that embed governance, security, and data-quality best practices across pipelines and storage layers. - Manage multiple customer accounts, oversee delivery milestones, and produce clear status reporting. - Diagnose data and system defects, perform root-cause analysis, and mentor internal engineers on quality and operational excellence.

Requirements - 7+ years as a hands-on Solutions Architect and/or Senior Data Engineer, with recent production experience on a major lakehouse platform. - Deep expertise with Spark, Hadoop, and Kafka, plus the surrounding data and ML tooling ecosystem. - Proficiency in Java, Python, and/or Scala, with strong SQL skills including debugging and optimization. - Hands-on experience with at least one of AWS, Azure, or GCP. - Strong client-facing communication, presentation, and documentation skills, including POCs, roadmaps, and architecture diagrams. - Track record of leading teams, mentoring engineers, and taking solutions into production at scale. - 4-year Bachelor's degree in Computer Science or a related field.

Nice to have - Background working in financial services, ideally in a banking environment, and familiarity with banking regulatory and compliance requirements. - Experience with distributed storage (S3, ADLS, HDFS, GCS, NoSQL systems) and integration tools such as Kafka, Fivetran, Matillion, NiFi, or Informatica. - Workflow orchestration experience with Airflow, Luigi, or NiFi, and automated transformation using dbt or Spark Structured Streaming. - Two or more certifications such as Associate Developer for Apache Spark, Data Engineer Associate, Professional Data Engineer, Machine Learning Associate, or Professional ML Engineer.

Benefits and work setup - Nationwide market-based base salary range plus performance-based cash incentive awards. - Health insurance for employees and eligible dependents starting on day one. - Minimum 15 days of paid time off plus 9 paid company holidays per year. - Opportunity to lead complex, high-visibility customer projects and grow technical and people-leadership skills.