Data Engineer

What will you do

• own end-to-end data platforms and critical data domains across Newtap's lending and financial ecosystem
• 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 junior 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 Newtap's Data 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

You should apply if you are:

• 4–7 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