Company Description
Curinos empowers financial institutions to make better, faster and more profitable decisions through industry-leading proprietary data, technologies and insights. With decades-long expertise in the financial services industry and a relentless focus on the future, Curinos technology and analytics ecosystem allows clients to anticipate customer needs and optimize their go-to market decisions in an increasingly competitive market.
Curinos operates in a hybrid/remote model, and this position is fully remote in the United States or hybrid in the Greater New York, Boston or Chicago metropolitan areas.
Job Description
We are seeking and experienced Staff Engineer – Data Platform and Lakehouse to lead the design and implementation of our cloud-native Data Platform that support AI, advanced analytics, and machine learning applications in our ecosystem of B2B SaaS applications in the FinTech vertical You’ll work with a diverse team of talented engineers, AI and ML scientists, and product managers to build our next generation data and AI platforms, support migration of products from legacy infrastructure, and help product engineering teams leverage the Data platform and Lakehouse to launch new products and build new genAI applications. As a company that specializes in data-driven insights, the reliability, scalability, and effectiveness of our data & AI platforms are integral to our product offerings. This role requires deep technical expertise, strategic thinking, and strong collaboration across engineering, data science, and product teams.

DESIRED SKILLS AND EXPERTISE:

• 15+ years of experience in software development, covering the full SDLC: ideation, design, development, testing, deployment, and support.
• Strategic mindset, capable of aligning technical decisions with business goals and driving architectural vision.
• Excellent collaboration and communication skills across functions, including data scientists, MLOps engineers, and product managers.
• Advanced proficiency in Python and SQL, with a strong foundation in software engineering principles.
• Extensive experience with distributed computing frameworks, particularly Apache Spark, including performance tuning and scalability.
• Required hands-on experience with Databricks, including Unity Catalog, Feature Store, and Delta Live Tables.
• Proficiency in data pipeline orchestration tools using Databricks Workflows, Airflow, or AWS Glue; Databricks required.
• Strong command of data engineering best practices and tools, including ETL/ELT design, data quality validation, monitoring, and observability. Experience with Monte Carlo preferred.
• Proven ability to build scalable, cloud-native data architectures on Databricks, AWS, Azure, or GCP; Databricks required.
• Experience enabling AI/ML workloads including feature engineering, model deployment, and real-time processing.
• Strong understanding of data governance and security, including access control, data lineage, compliance, and metadata management.
  Additional Information

• Design and implement scalable, secure, and maintainable data platforms on Databricks and AWS cloud infrastructure.
• Provide architectural leadership across engineering domains, ensuring consistency, scalability, and resilience
• Architect distributed data processing systems using Apache Spark and optimize for performance and scalability.
• Lead development of reusable data pipelines and workflows using Databricks Workflows.
• Translate business objectives into platform capabilities in collaboration with Product Managers and cross-functional teams.
• Support AI/ML initiatives through robust data engineering, including feature engineering, model deployment.
• Champion best practices in ETL/ELT, data quality, monitoring, observability, and Agile development.
• Drive adoption of data governance standards: access control, metadata management, lineage, and compliance.
• Establish and maintain CI/CD pipelines and DevOps automation for data infrastructure.
• Evaluate and integrate emerging technologies to enhance development, testing, deployment, and monitoring.