• Analysis and Planning of Loads/Pipelines: • Assess the data warehouse architecture and requirements. • Map data, transformations and processes in GCP services (Cloud Storage, BigQuery, Dataproc). • Define data migration strategy (full load, incremental, CDC). • Develop a data architecture plan on GCP. • Design and Data Modeling on GCP: • Design table schemas in BigQuery, considering performance, cost and scalability. • Define partitioning and clustering strategies for BigQuery. • Model data zones in Cloud Storage (Bronze, Silver and Gold). • ELT/ETL Pipeline Development: • Create data transformation routines using Dataproc (Spark) or Dataflow to load data into BigQuery. • Translate business logic and existing transformations into GCP. • Implement data validation and data quality mechanisms. • Infrastructure Provisioning and Management: • Use IaC tools (Terraform) to provision and manage GCP resources (BigQuery datasets/tables, Cloud Storage buckets, Dataproc clusters). • Configure and optimize Dataproc clusters for different workloads. • Manage networking, security (IAM) and access in GCP. • Performance and Cost Optimization: • Optimize queries in BigQuery to reduce costs and improve performance. • Tune and optimize Spark jobs on Dataproc. • Monitor and optimize GCP resource usage to control costs. • Data Security and Governance: • Implement and ensure data security in transit and at rest. • Define and apply IAM policies to control access to data and resources. • Ensure compliance with data governance policies. • Monitoring and Support: • Troubleshoot performance and functional issues of data pipelines and GCP resources. • Documentation: • Document the architecture, data pipelines, data models and operational procedures. • Communication: • Communicate effectively with team members, stakeholders and other areas of the company. • Ensure clear communication between architecture definitions and software components, and the evolution and quality of the team's deliverables. • Jira / Agile Methodologies: • Be familiar with agile methodologies, their ceremonies, and be proficient with the Jira tool.

Role Description Dr. Berg Nutritionals is doing over nine-figures across Amazon, Shopify, Walmart, and TikTok Shop, building on top of a YouTube channel with 15M subscribers and 111M weekly views. We are building a unified data platform that powers executive reporting, internal operations, customer-facing AI products, and the experimentation layer that comes after that. You are the foundational hire who makes that platform real. Not a contributor on a data team, but the senior owner of the platform itself, reporting directly to the CIO. This role is unusual because: - You will be the senior data authority. - There is no Head of Data above you, and we are not promising one. - You report to the CIO, set technical direction, and are accountable for the platform's trustworthiness. - Your tables feed real decisions, today. - The scope is broader than analytics. - You're building the trusted data foundation for everything the engineering department ships. If you've ever wanted the autonomy of a founding data engineer without giving up the salary or the scale of a real business — this is that role. What You'll Do In your first 90 days you'll: - Audit every existing data source (what's flowing, what's broken, what's missing). - Replace our manual CSV-based Klaviyo ingestion with a direct API pipeline. - Stand up production pipelines for Amazon SP-API, Shopify, and NetSuite with proper monitoring. - Establish our infrastructure-as-code practice and CI/CD pipeline. - Define the target architecture: ingestion patterns, orchestration standards, environment separation, data-quality gates, deployment workflow. After that, you own: - Ingestion: Amazon SP-API, Shopify Admin API, NetSuite SuiteAnalytics Connect, Klaviyo, Recharge, YouTube, GA4 (BigQuery export), Google Search Console, Google Ads, Meta Ads, Triple Whale, plus ~15 more. - Orchestration: Decide what runs when based on finance needs and API quotas. - Reliability: Health checks, schema validation, freshness SLAs, source reconciliation, data-quality gates. - Cost and performance: Partitioning, query tuning, and incremental processing to save costs. - Data contracts: Ensure pipelines stop and alert when a source violates its contract. Qualifications - 8–12+ years in data engineering, with at least 4 years primarily in Azure (Data Factory, Synapse, Fabric, Functions, ADLS, Azure Monitor, or comparable). - Demonstrated ownership of a production data platform, not just a contributor. - Strong SQL experience — query tuning, execution plans, indexing strategy, incremental models, data-quality validation. - Production-level C# and/or Python experience for custom connector work. - Solid experience with messy commerce/ERP APIs — ideally Amazon SP-API, NetSuite, or similar. - IaC and CI/CD discipline — Bicep, Terraform, or ARM, with dev/staging/prod separation, peer review, rollback thinking. - Demonstrated on-call experience — owned production incidents and written runbooks. - Experience partnering with a technical executive without layers of management. Requirements - This role is probably not for you if you want a Head of Data above you to set direction. - Your experience is mostly clean SaaS APIs and you've never wrestled a real ERP. - You prefer being one of several data engineers on a mature team. - You want to build the AI itself; this role is the foundation it stands on. Work from Home Requirements - Up-to-date Mac or Windows computer with anti-virus protection and a reliable high-speed internet connection. - Quiet, distraction-free workspace. Compensation & Benefits - Pay Range: $185,000–$225,000 base, plus performance bonus. - Comprehensive health, dental, vision. - Generous PTO. Fully remote within the continental US. - Direct access to the CEO, CIO, Finance Director, and COO. - Budget for training, conferences, and tools. - Meaningful product discount on Dr. Berg Nutritionals products. Hours of Work Must be available during business hours, Monday – Friday, 9am – 6pm EST. Engagement Type Employee (W-2) full-time salaried, exempt. Dr. Berg Nutritionals is an equal-opportunity employer. We welcome applicants from all backgrounds. We are not currently hiring international or California-based employees. Applicants must be legally authorized to work in the United States for any employee (W-2) positions.

• Conduzir apresentações técnicas, workshops e treinamentos sobre soluções de dados na GCP; • Desenvolver e executar provas de conceito (PoCs) e demonstrações hands-on; • Apoiar clientes na construção de arquiteturas modernas de dados (data platforms); • Atuar na disseminação de soluções de Data Analytics, Machine Learning e AI; • Trabalhar em conjunto com Customer Engineers e Account Executives na qualificação de oportunidades; • Apoiar estratégias de adoção de produtos de dados da GCP; • Criar conteúdos técnicos e materiais de treinamento; • Interagir com clientes e parceiros em toda a América Latina; • Promover boas práticas em engenharia e arquitetura de dados na nuvem;

• Architect and optimize the **Snowflake data platform**, including warehouse sizing, cost optimization, storage strategy, and access controls • Design and own **dbt project structure**, including models, macros, testing, documentation, and scalable data contracts • Build and maintain **ELT pipelines** using Fivetran and orchestration tools, ensuring reliable data ingestion across multiple sources • Implement and manage **data quality and observability frameworks** (tests, SLAs, lineage, monitoring, incident response) • Translate **business requirements into scalable data models and reusable datasets** • Partner with **Analytics, Product, and Marketing teams** to deliver high-quality, self-service data solutions • Establish and enforce **data modeling standards** (dimensional and ER models) • Optimize **query performance and warehouse costs** in Snowflake, providing insights to stakeholders • Define and enforce **data governance policies**, including RBAC, masking, and PII handling • Own end-to-end delivery of **complex data initiatives**, from design to production • Participate in **code reviews and technical design discussions**, raising engineering standards • Identify and reduce **technical debt** across pipelines, models, and infrastructure