• Perform data modeling by analyzing and structuring datasets to ensure proper relationships and adherence to industry best practices. • Handle special data by creating and managing equivalence tables, including specific cases such as “verticals”, aiming for greater standardization and efficiency. • Build pipelines that ensure full traceability from client file delivery through to loading into the Gold layer in Azure Databricks. • Identify opportunities for source-side automation to reduce reliance on manual files and increase efficiency. • Propose and implement efficient migration strategies, such as incremental loads, to avoid high costs from daily full loads. • Familiarity with code documentation. • Experience with agile methodologies. • Familiarity with developing process monitoring and alerts. • Ability to communicate technical solutions in a simple, pragmatic manner. • Proactive in supporting the team.

• Organization and Structuring of Data Repositories • Perform data modeling, analyzing and structuring datasets to ensure proper relationships and adherence to market best practices • Handle special data cases by creating and managing equivalence/mapping tables, including specific cases such as “verticals,” aiming for greater standardization and efficiency • Development of Data Pipelines and Flows • Build pipelines that ensure full traceability, from client file delivery through to loading into the Gold layer on Google Cloud Platform (GCP) • Identify opportunities for source-side automation to reduce dependence on manual files and increase efficiency • Migration and Optimization of Databases • Propose and implement efficient migration strategies, such as incremental loads, avoiding high costs associated with daily full loads • Familiarity with code documentation (desirable) • Experience with agile methodologies (desirable) • Skills in developing and optimizing APIs for data consumption (desirable) • Familiarity with developing monitoring and alerts for processes implemented on GCP (desirable) • Ability to communicate technical solutions in a simple and pragmatic way (soft skill) • Proactive in supporting the team (soft skill)

• Design, implement, and maintain robust, scalable data pipelines using Google Cloud Platform (GCP) and Amazon Web Services (AWS). • Develop and implement effective data models to support complex analytics and automated reporting. • Work closely with product teams and squads to understand business requirements and translate them into viable technical solutions. • Ensure data governance, quality, and compliance with established standards by implementing data engineering best practices. • Implement efficient ETL/ELT processes and optimize the performance of data pipelines to ensure data accuracy and integrity. • Provide technical support to resolve data integration issues and ensure continuous availability of data systems. • Actively participate in agile initiatives, collaborating in sprints and ceremonies to ensure high-quality, on-time deliveries.

• Design and implement robust, production-grade pipelines using Python, Spark SQL, and Airflow to process high-volume file-based datasets (CSV, Parquet, JSON). • Lead efforts to canonicalize raw healthcare data (837 claims, EHR, partner data, flat files) into internal models. • Own the full lifecycle of core pipelines — from file ingestion to validated, queryable datasets — ensuring high reliability and performance. • Onboard new customers by integrating their raw data into internal pipelines and canonical models; collaborate with SMEs, Account Managers, and Product to ensure successful implementation and troubleshooting. • Build resilient, idempotent transformation logic with data quality checks, validation layers, and observability. • Refactor and scale existing pipelines to meet growing data and business needs. • Tune Spark jobs and optimize distributed processing performance. • Implement schema enforcement and versioning aligned with internal data standards. • Collaborate deeply with Data Analysts, Data Scientists, Product Managers, Engineering, Platform, SMEs, and AMs to ensure pipelines meet evolving business needs. • Monitor pipeline health, participate in on-call rotations, and proactively debug and resolve production data flow issues. • Contribute to the evolution of our data platform — driving toward mature patterns in observability, testing, and automation. • Build and enhance streaming pipelines (Kafka, SQS, or similar) where needed to support near-real-time data needs. • Help develop and champion internal best practices around pipeline development and data modeling.