• Design and implement **scalable streaming data platforms** to support real-time ingestion, processing, and analytics • Architect and guide the development of **end-to-end data platforms** across batch and streaming workloads • Lead and contribute to **Master Data Management (MDM)** solutions, including: • Golden record design • Data matching, survivorship, and hierarchy management • Integration patterns with downstream consumers • Define and implement **data governance frameworks**, including: • Data ownership and stewardship models • Data quality rules and monitoring • Metadata, lineage, and access controls • Collaborate with application teams to expose data via **APIs and event-driven architectures** • Provide architectural guidance for **cloud-native deployments**, including containerization and orchestration • Establish **data architecture standards, patterns, and best practices** • Partner with DevOps teams to enable CI/CD, infrastructure automation, and platform reliability • Review designs, mentor engineers, and help drive technical decisions across projects

• Build data pipelines: Develop robust batch and streaming pipelines (Kafka/Kinesis) to ingest, transform, and enrich large-scale event data (impressions, clicks, conversions, costs, identity signals). • Create data aggregates & marts: Design and maintain curated aggregates and dimensional models for multiple consumers—prediction models, agents, BI dashboards, and measurement workflows. • Data modeling & contracts: Define schemas, data contracts, and versioning strategies to keep downstream systems stable as sources evolve. • Data quality & reliability: Implement validation, anomaly detection, backfills, and reconciliation to ensure completeness, correctness, and timeliness (SLAs/SLOs). • Performance & cost optimization: Optimize compute/storage for scale (partitioning, file sizing, incremental processing, indexing), balancing latency, throughput, and cost. • Orchestration & automation: Build repeatable workflows with scheduling/orchestration (e.g., Airflow, Dagster, Step Functions) and CI/CD for data pipelines. • Observability for data systems: Instrument pipelines with metrics, logs, lineage, and alerting to accelerate detection and root-cause analysis of data issues. • Security & governance: Apply least-privilege access, PII-aware handling, and governance controls aligned with enterprise standards.

• Design and maintain ingestion pipelines (Python-based ETL/ELT) • Design structured transformation workflows using dbt, SQLMesh, or equivalent • Convert unstructured transcripts and documents into normalized database records • Maintain PostgreSQL architecture (structured tables, JSONB, indexing strategy) • Develop attribute extraction frameworks for technical, commercial, and risk signals • Ensure data quality, consistency, and lineage from raw interaction to structured output • Collaborate with AI/ML engineers to ensure clean model inputs

• Own and evolve Koala Health’s end-to-end data infrastructure, including ingestion, transformation, modeling, and delivery. • Design and maintain reliable data pipelines from production systems (e.g., application databases, third-party tools, vendors). • Build and manage data models that support analytics, reporting, and operational use cases. • Establish and enforce best practices for data quality, testing, monitoring, and documentation. • Partner with stakeholders across product, operations, finance, and marketing to understand data needs and translate them into scalable solutions. • Improve the reliability, performance, and cost-efficiency of the data stack as the business grows. • Own incident response and debugging for data issues, proactively identifying and resolving root causes. • Create and maintain clear documentation so data assets are understandable and usable across the company. • Evaluate and implement tooling improvements where it meaningfully improves developer velocity or data quality. • Act as a thought partner to leadership on how data can better support decision-making and operational efficiency.