About UsHealth is the most important thing in life, and the American healthcare system is completely broken - poor outcomes, high cost, bad patient experience. We're building a new system from the ground up.Our mission is to improve people’s health by making it easy to live a healthy lifestyle. Nourish is the country's largest dietitian-led metabolic health clinic. We’re an AI-native digital health system matching patients with 10,000+ Registered Dietitians, physicians, medications, lab testing, and AI agents to deliver insurance-covered care across all 50 states. Founded four years ago, we've completed millions of appointments, tripled year-over-year, and partnered with health plans covering 200M+ Americans across 250+ health systems. In 2026 we raised a $100M Series C, bringing total funding to $215M. The round was led by Menlo Ventures, with participation from Thrive Capital, Index Ventures, J.P. Morgan Growth Equity Partners, Maverick Ventures, Y Combinator, BoxGroup, Atomico, Daybreak, and Operator Partners. Learn more about our Series C here: Nourish Blog, Bloomberg, Fierce Healthcare, Digital Native, The Pulse Podcast. This is not a job for everyone. We hold an extremely high bar because we believe talent density is our biggest competitive advantage. We're looking for people who actively choose hard, ambiguous problems, who run toward unglamorous work, give and receive candid feedback, and bring relentless resilience without the ego. Our work is important, but we are not self-important. We do this because we’re solving one of the hardest problems in the world, and the problem matters. If that's you, we disproportionately reward it. About the RoleWe are hiring a Senior or Staff Data Engineer to become the first dedicated data engineering hire on Nourish's Data team. This is a high-impact platform role where you'll build the foundation that makes our data faster, more reliable, easier to trust, and more cost efficient as we scale. Our stack includes RudderStack and Fivetran for ingestion, Snowflake as our warehouse, dbt for transformations, and Omni and Metabase for BI. As AI becomes a core way our teams interact with data, you'll also help build the infrastructure, governance, and semantic layer that enable both humans and AI agents to safely and efficiently access trusted data. You'll partner cross functionally to improve the entire data platform - from ingestion and modeling to observability, governance, and production data workflows. This is a full-time role and open to NYC-based candidates (2-3 days/week in office), with exceptional remote candidates considered. Our office is located in Gramercy. Key Responsibilities: - Own and improve data ingestion and orchestration across our modern data stack. - Build a scalable, reliable, and cost-efficient Snowflake platform. - Partner on dbt architecture, testing, CI/CD, and data modeling best practices. - Improve observability, monitoring, and platform reliability. - Build the governance, semantic layer, and self-service capabilities that power trusted analytics and AI. - Help establish production-ready infrastructure for AI and machine learning workloads. - Turn recurring operational pain points into automation, tooling, and reusable platform capabilities. We'd love to hear from you if: - You have 5+ years of experience building modern data platforms or infrastructure. - You've owned production data pipelines and cloud data warehouses. - You're highly proficient in SQL and have experience with Snowflake and dbt (or similar technologies). - You've improved platform reliability, performance, and cost through thoughtful architecture and automation. - You care about building trusted, well-governed data systems that enable self-service analytics. - You're excited about the role AI will play in the modern data platform. - You communicate technical tradeoffs clearly and enjoy enabling other engineers and business teams. More InformationThe Nourish Bar Our Values Why Nourish Exists How We Work Comp Philosophy Benefits Please note that you must be legally authorized to work in the U.S. for this position.

Role Description We are building a multi-site platform that orchestrates telecoms services for mobile operators across different regions, with additional network nodes in other regions. As our DBA & Bulk Data Management Engineer you will own every database that underpins this platform from the core BSS/OSS/NOC modules through to the PostgreSQL stores that sit behind our different applications. You will also extend your ownership into the network control layer, maintaining interfaces to vendor-managed databases inside HSS, OCS, PCRF, and SMSC nodes, and ensuring the mediation pipelines that bring network data into the other required system are reliable, complete, and governed by clear data-handling policies. This role spans three disciplines: - Hands-on database engineering (installation, tuning, backup/restore, high availability) - Bulk data management (large-scale loads, migrations, exports, purges) - Data governance (designing and enforcing retention, archival, and compliance policies) You will be a senior individual contributor and the go-to expert for all database topics across the entire BSS and NOC stack. Qualifications - Strong hands-on PostgreSQL administration: installation, configuration, replication (logical/streaming), upgrade, and vacuum tuning - Proven backup and restore experience: pg_dump, pg_basebackup, WAL archiving, point-in-time recovery, and DR testing - Query performance analysis: EXPLAIN/ANALYZE, index design (B-Tree, partial, composite), and schema-level optimisation - Bulk data operations: large COPY/INSERT loads, logical replication for live migrations, and automated purge/archival pipelines - Data governance: designing and documenting retention, archival, masking, and access-control policies - Scripting in Python or Bash for automation of backup jobs, health-check scripts, and bulk-load utilities - Experience with containerized database deployments (Docker / Kubernetes) - Monitoring: setting up Prometheus postgres_exporter, building Grafana dashboards for database KPIs - High-availability patterns: replication failover, connection pooling (PgBouncer / Pgpool), read-replica routing - Ability to work independently across multiple concurrent projects in a distributed, async-first team - Multi-datacenter replication and active-passive failover between geographically distributed clusters Requirements - Experience with telecoms BSS/OSS platforms (billing, subscription management, or mediation systems) - Familiarity with network-element databases: HSS (HLR), OCS, PCRF, SMSC, or PGW data models - Data compliance experience (GDPR subscriber data, CDR retention regulations in EU jurisdictions) Benefits - Fully remote - An exciting and challenging greenfield platform with great skill and knowledge development opportunities - The opportunity to join a team of highly professional specialists in an international environment - The opportunity for professional development within a reputable international innovative and growing company

• Assist TSD with data products by providing highly skilled and authoritative expertise on data engineering methods and best practices, including code-first development approaches and modern pipeline design patterns. • Design, implement, and maintain an efficient, secure, stable, and flexible data architecture that supports products and end-users, with all assets managed via source control. • Design, implement, and maintain ELT/ETL pipelines for efficient processing of source data in Azure Synapse and Azure Machine Learning (using SDK V1 and SDK V2) • Review, maintain, and improve existing architecture and pipelines, including periodic audits to address bottlenecks, deprecated dependencies, and architecture drift. • Establish quality controls for maintaining all pipelines, and introduce error handling, logging mechanisms, and validation checks. • Incorporate source control for all pipelines and data analytics codebases to enable iterative code development while ensuring data architecture stability. • Optimize the ingestion, processing, and storage of a wide variety of datasets and data types, including modern columnar formats such as Parquet. • Develop self-service capabilities for SBA OIG analysts to query and export data for investigations and audits. • Coordinate with data scientists to ensure the architecture efficiently supports machine learning algorithms and data pipelines in Azure Machine Learning. • Develop robust standard operating protocols (SOPs) dictating the authoring, development, validation, publishing, execution, and monitoring of all data pipelines and assets in Azure environment. • Provide detailed documentation of the data architecture, including data dictionaries, ER diagrams, and pipeline process maps. • Maintain and expand the environment with additional datasets and services upon request, following a defined intake and testing process prior to production deployment. • Stay current with emerging AI tools relevant to data engineering and contribute to exploratory efforts evaluating automation and LLM-assisted capabilities.

Role Description You will be: - Designing and provisioning the lakehouse foundation on AWS: S3 lake zones (raw, canonical, curated), Apache Iceberg tables, Glue Data Catalog, Athena, and Lake Formation. - Delivering all infrastructure as code with Terraform, reproducible from the client's GitHub repositories and CI/CD, across isolated environments (ci, dev, staging, prod). - Building and enforcing the tenant-isolation security gate: Lake Formation row-level security and physical partitioning by CompanyId, separate IAM roles for customer versus internal access, and fail-closed handling that quarantines and alerts on rows with missing or unresolved CompanyId. - Implementing the upstream entitlement mapping (principal to allowed CompanyIds) that drives access control. - Setting up ingestion infrastructure: CDC paths from DynamoDB Streams, AWS DMS extracts from Aurora PostgreSQL, and bulk export to Parquet, supporting the near-real-time (5 min) and batch (4 hr) SLAs. - Standing up workflow orchestration with Apache Airflow and Astronomer Cosmos, including profile-based connections, model-level retries, and lineage emission. - Building CI/CD pipelines (GitHub Actions): dbt compile and slim builds, SQLFluff linting, DAG validation, branch protection, environment promotion, and Git-revert rollback. - Configuring end-to-end governance and observability: catalog and data contracts, OpenLineage capture, and org-wide audit through CloudTrail. - Owning cost governance and monitoring: resource tagging, usage alerting, and right-sizing of compute. - Collaborating with the Data Platform Architect, Data Engineers, and Analytics Engineer, and support knowledge transfer to the client team. Qualifications - Strong experience with AWS data and platform services: S3, Glue (Data Catalog and Jobs), Athena, Lake Formation, IAM, VPC networking, DMS, DynamoDB, Aurora PostgreSQL. - Experience with infrastructure as Code with Terraform, including modular design and YAML-driven configuration. - Knowledge about Apache Iceberg and open table formats on S3. - Experience with security and access control. - Knowledge about CI/CD engineering with GitHub Actions, trunk-based development, and environment promotion. - Solid Python and SQL skills. - Experience with dbt on AWS (dbt-athena, dbt-glue adapters) and analytics-engineering workflows. - Previous exposure to Airflow orchestration, ideally with Astronomer Cosmos. - Data lineage and observability (OpenLineage, dbt-elementary) and data quality tooling (dbt tests, dbt-expectations) skills. - Experience with CDC and streaming ingestion patterns. Requirements - Work from the European Union region and a work permit are required. Nice to have - Exposure to BI serving layers such as QuickSight or Sigma. - Snowflake experience. Recruitment Process - CV review - HR call - Interview - Client Interview - Decision