• Collaborative Architecture: Partner with the team to provide architectural suggestions and formal proposals for our core data systems. You will help ensure a seamless flow between microservices, our data lake, and downstream analytics. • Modern Pipeline Development: Build and ship production-level data pipelines using PySpark and SQL. You will collaborate on establishing standards for idempotency, monitoring, and performance tuning. • Lakehouse Data Modeling: Implement robust data modeling patterns (Medallion architecture: Bronze/Silver/Gold). You will ensure our Lakehouse is not just a data dump, but a high-performance source of truth for both BI and ML. • Databricks & Lakehouse Evolution: Contribute to the continuous improvement of our Databricks environment, with a focus on Delta Lake optimization and robust governance via Unity Catalog. • Systems Evolution & Stability: Act as the steward of our production environment. You will lead the refactoring of legacy pipelines to improve observability, reduce technical debt, and ensure seamless data flow between our microservices and the Databricks Lakehouse. • Technical Influence: Work closely with the Director of Data to refine our technical roadmap. You will lead by example through deep-dive code reviews and by maintaining a high bar for technical documentation. • Reliability & Governance: Conduct comprehensive audits to identify system inefficiencies. You will share ownership of data quality, security, and privacy across the entire lifecycle of our datasets.

• Work on strategic projects involving migration, integration, automation, and process improvement • Design, implement, and maintain data pipelines connecting OMS/EMS, risk/performance systems, internal platforms, custodians, and administrators • Build data pipelines capable of identifying business opportunities from internal and external data • Build and maintain high-reliability, high-performance ETL/ELT processes • Develop integrations using APIs, SFTP, messaging systems, or proprietary connectors • Document processes, data flows, and technical solutions for future reference, ensuring knowledge is shared and preserved • Collaborate with business analysts to define calculation rules, granularity, data quality standards, and key fields • Identify and implement continuous improvements focused on efficiency, performance, governance, and cost, delivering business value

• **Build** scalable, resilient, high-performance data pipelines on Databricks and AWS. • **Develop** solutions in Python and SQL, applying best practices, standards, and automation. • **Implement** data models and processing layers based on Lakehouse architecture. • **Optimize** Spark jobs, ensuring efficiency, governance, and reliability of workloads. • **Integrate** different data sources, ensuring security, quality, and compliance. • **Collaborate** with architects and engineers to define technical solutions, standards, and engineering practices. • **Support** platform user squads by providing technical guidance and adoption support. • **Monitor** pipelines, metrics, and performance, proactively addressing issues. • **Document** solutions, flows, processes, and engineering standards. • **Contribute** to modernization, automation, and continuous improvement initiatives for the Data & AI Platform.

• Build scalable and reliable data pipelines that collect, transform, load and curate data from internal systems • Augment data platform with data pipelines from external systems. • Ensure high data quality for pipelines you build and make them auditable • Drive data systems to be as near real-time as possible • Support design and deployment of distributed data store that will be central source of truth across the organization • Build data connections to company's internal IT systems • Develop, customize, configure self service tools that help our data consumers to extract and analyze data from our massive internal data store • Evaluate new technologies and build prototypes for continuous improvements in data engineering.