• Upload and organize patient records from multiple data sources (CSV, spreadsheets, clinical documents) • Prepare and structure large datasets for system migration • Ensure accurate and efficient data transfer into the platform • Clean and standardize datasets from external systems • Identify missing, duplicate, or inconsistent data • Normalize formats (names, dates, medical data) • Validate data accuracy before and after migration • Utilize AI tools to extract and process patient data • Generate structured patient records • Review and validate AI-generated outputs for accuracy • Perform detailed QA checks to ensure near-zero error rates • Identify and resolve issues proactively before they impact implementation • Maintain high standards of data integrity and consistency • Update implementation trackers and progress reports • Communicate updates, blockers, and issues clearly with the team.

• Assist in developing and maintaining data pipelines and ETL/ELT processes under the guidance of more senior engineers. • Write Python and SQL to extract, transform, validate, and load data from common sources. • Perform data quality checks (validation, reconciliation, basic monitoring) and help troubleshoot data issues. • Develop dashboards and analytic products using data visualization tools (e.g., Power BI, Tableau). • Support cloud-based data workloads (e.g., Azure/AWS/GCP basics) and learn platform-native services and patterns. • Document pipeline steps and technical processes to support maintainability and knowledge transfer. • Participate in team delivery rhythms (standups, sprint ceremonies) and contribute to reviews with a learning mindset. • Design, build, test, and maintain scalable data pipelines (batch and/or streaming as applicable) with increasing independence. • Integrate data from multiple sources, resolve inconsistencies, and deliver curated datasets for analytics and operational use. • Own data quality for assigned domains by implementing validation checks, reconciliation, and monitoring/alerting patterns. • Build, maintain, and deploy data products for analytics and data science teams on cloud platforms (e.g. AWS, Azure, GCP). • Optimize performance of pipelines and queries (tuning, partitioning patterns, efficient compute usage). • Collaborate cross-functionally with analysts, data scientists, and stakeholders to translate requirements into technical designs and delivery plans. • Produce and maintain technical documentation for data flows, data models, and operational procedures. • Contribute to governance and compliance practices (access controls, lineage awareness, controlled data handling) within your scope. • Lead the design and build of scalable data pipeline architectures and tools, including patterns for reliability, security, and maintainability. • Drive ETL/ELT and data quality strategy (frameworks, standards, repeatable testing/monitoring approaches) and raise engineering maturity across the team. • Architect solutions in cloud data platforms (e.g., Azure + Databricks, Snowflake) and guide implementation tradeoffs (cost, performance, scalability, governance). • Design data stores and interactions across storage types (relational, warehouse, lake/lakehouse, and NoSQL where needed) aligned to use cases. • Enable data science / ML readiness by delivering well-modeled, reliable, well-documented datasets and features. • Lead requirements gathering and technical planning; translate ambiguous problem statements into actionable architectures, backlogs, and delivery increments. • Champion data quality and governance standards through the development of sophisticated data quality frameworks, dashboards, and feedback loops to ensure transparency in data completeness, consistency, and quality for partners and researchers. • Own client and stakeholder engagement for your workstream, including organizing/leading meetings, producing clear written outputs, and tracking follow-through. • Mentor and review: provide strong code/design reviews, coach engineers, and help remove technical blockers.

• Lead, mentor, and grow a team of data engineers and architects • Define and execute the technical roadmap for production database systems (MySQL, PostgreSQL, DynamoDB, Elastic) • Own the architecture and governance of binlog replication, logical replication, and CDC workflows • Drive strategy and reliability for ELT/ETL pipelines and Kafka-based streaming architectures • Set standards for performance optimization, query tuning, indexing, and database scaling across teams • Oversee backup, failover, disaster recovery (PITR), and incident response for all production data systems • Drive cost efficiency, infrastructure optimization, and monitoring across cloud-managed data services (AWS RDS, Aurora, DynamoDB) • Champion data integrity, security, and compliance standards across all data engineering work • Partner cross-functionally with backend, data science, infrastructure, and product teams to align on data platform priorities • Establish engineering guardrails, best practices, and documentation to enable team autonomy and quality at scale • Lead the evaluation and selection of next-generation data warehousing technology (Snowflake, Databricks, AWS Redshift Serverless) — assessing performance, cost, ecosystem fit, and migration complexity to inform a platform decision • Own the design of an upgraded data model for the warehouse in partnership with data engineers and architects, establishing standards for schema design, partitioning, access patterns, and downstream consumption • Oversee the end-to-end migration from the current Redshift warehouse — planning the phased approach, managing cutover risk, and ensuring continuity of downstream reporting and analytics throughout

• Assist in building and maintaining ETL/data pipelines using Python and PySpark • Ingest, transform, and validate data from multiple sources • Support data modeling and schema design for structured datasets • Use Git for version control and collaborate with engineering teams • Perform unit testing, code reviews, and performance optimization • Contribute to technical documentation of data workflows and pipelines • Support feature testing and controlled releases in QA/dev environments • Perform exploratory analysis using Jupyter/Amazon SageMaker notebooks • Work in a Scrum/Agile environment with clear communication and collaboration