Data Engineer

Responsibilities:

• Own end-to-end data platforms and critical data domains.
• Design, build, and operate scalable data pipelines powering business reporting, analytics, risk, underwriting, collections, finance, compliance, and operational decision-making.
• Own and evolve Airflow orchestration, EMR/Spark processing, Athena query platforms, Iceberg Lakehouse tables, CDC ingestion frameworks, and Data Lake architecture.
• Design and maintain scalable data marts, curated datasets, semantic models, and data products consumed by business and technology teams.
• Drive improvements in platform reliability, observability, scalability, performance, cost efficiency, and data quality.
• Lead root cause analysis and resolution of complex data quality issues, operational incidents, reconciliation gaps, and platform bottlenecks.
• Collaborate closely with Product, Risk, Finance, Analytics, Compliance, Engineering, and Leadership teams to translate business requirements into scalable data solutions.
• Mentor L3 engineers through design reviews, code reviews, operational guidance, and engineering best practices.
• Drive adoption of engineering standards around testing, deployment, observability, governance, security, documentation, and operational excellence.
• Leverage AI-assisted engineering workflows to accelerate development, debugging, testing, documentation, and platform operations.
• Contribute to the evolution of lakehouse architecture, governance framework, metadata management, and AI-ready data foundations.
• Drive technical decisions within their domain while balancing scalability, maintainability, reliability, and business outcomes.

Requirements:

• 4-6 years of experience in data engineering, analytics engineering, platform engineering, or backend engineering with significant production ownership.
• Strong experience designing and operating production-grade data pipelines and distributed data processing systems.
• Hands-on experience with Airflow, Spark, EMR, Athena, data lakes, and cloud-native data platforms.
• Experience designing data marts, analytical datasets, and scalable data models for business consumption.
• Strong understanding of data lakehouse architectures, CDC patterns, schema evolution, partitioning strategies, and performance optimization.
• Experience owning production systems, troubleshooting incidents, and driving operational excellence.
• Ability to independently solve ambiguous technical problems and drive solutions from concept to production.
• Experience mentoring engineers and influencing engineering standards within a team.
• Strong stakeholder management skills with the ability to align technical and business priorities.
• Ability to leverage AI effectively for development, debugging, analysis, and productivity improvements.
• Interest in building scalable data platforms within fintech and regulated environments.