Data Engineer

• Design and develop scalable, cloud-native data architectures in BigQuery, ensuring optimal performance, maintainability, and cost efficiency
• Build, automate, and maintain ETL/ELT pipelines using modern data stack tools and custom solutions
• Create and optimize business-critical data marts, aggregations, and analytical datasets for reporting, dashboarding, and product insights
• Implement data quality frameworks, monitoring systems, logging, and alerting mechanisms to ensure data reliability
• Continuously improve performance, scalability, and cost efficiency of production data infrastructure
• Partner closely with Data Science teams to build reliable data services, infrastructure, and pipelines supporting machine learning and advanced analytics initiatives
• Collaborate with product, analytics, and engineering teams to define and deliver scalable data solutions across the organization

• Design and implement scalable data platforms and pipelines across cloud environments
• Develop reliable batch, streaming, and near-real-time pipelines using technologies such as Spark and Delta Lake
• Build ingestion, transformation, and curation workflows for both structured and unstructured data
• Implement modern data architectures including lakehouse patterns and medallion layering
• Deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems
• Enable data pipelines for GenAI use cases
• Design scalable logical and physical data models
• Orchestrate workflows using tools such as Airflow, dbt, Lakeflow, or equivalents
• Apply modern architecture patterns including event-driven and streaming architectures
• Establish strong data observability, including monitoring of data freshness, pipeline reliability, and SLA adherence
• Enable data serving layers to support downstream systems

Senior Data Engineer

• Assist the Data Engineering team with migration from BigQuery to Snowflake
• Translate and optimize SQL queries across platforms
• Perform end-to-end validation to ensure data correctness and pipeline reliability
• Write and maintain incremental dbt models
• Collaborate closely with data engineers, analysts, and stakeholders to understand data needs and deliver solutions
• Participate in code reviews, share knowledge, and contribute to team best practices
• Document work clearly to support maintainability and knowledge sharing

Junior Data Engineer

• Design, build, and maintain the reporting data warehouse using a layered model: bronze, silver, gold, semantic, and governance.
• Build reliable ELT/ETL pipelines from ERP, CRM, production, budget, quality, catalog, and organization data sources.
• Develop and optimize SQL transformations for daily facts, conformed dimensions, KPI views, cohort analytics, client 360 views, alerting views, and operational dashboards.
• Maintain semantic views used by Power BI reporting, ensuring business users only access approved reporting layers.
• Implement data quality checks, freshness validation, reconciliation tests, and SLA monitoring.
• Support daily and hourly refresh pipelines, including incremental refresh logic for operational order and production data.
• Implement governance, role-based access, and secure data access patterns for group, lab, clinic, owner, and user-level visibility.
• Partner with BI developers to ensure Power BI reports are backed by clean, stable, documented semantic datasets.
• Collaborate with business stakeholders to validate KPI definitions, reporting grain, ownership logic, and actionability of alerts.
• Maintain documentation for data lineage, metric definitions, pipeline runbooks, and acceptance tests.

Senior Data Engineer – Part-Time

• Analyze business workflows and identify opportunities for data automation and AI-driven process automation
• Design, build, and maintain scalable data pipelines and integrations, including ingestion from unreliable or unstructured 3rd party sources
• Build LLM- and agent-based solutions for data transformation, validation/testing, and extraction tasks
• Containerize data and AI workloads using Docker and deploy to cloud infrastructure (AWS)
• Develop prototypes and POCs to validate ideas quickly — both data pipelines and AI-powered workflows
• Collaborate with business and technical teams to refine requirements and iterate on solutions
• Support the deployment and integration of data and AI solutions into production systems
• Continuously improve data processes through automation and AI-driven approaches
• Contribute to data modeling, quality, and observability practices

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Data Engineer

Senior Data Engineer

Junior Data Engineer

Senior Data Engineer – Part-Time

Data Engineer, AI Experience