Data Architect

Job Description

At the FoodHealth Company (formerly bitewell), we’re not building another food app — we’re on a mission to fundamentally change the way the world eats. Our vision? A world without diet-related disease. And with $7.5M in Series A funding led by Reach Capital and Ulu Ventures, we’re poised to make it happen.

Our flagship product, the FoodHealth Score, is the most widely distributed retail nutrition rating in the U.S., providing an instant reflection of the nutritional quality of any food — already live in Kroger stores like King Soopers, Ralphs, and Harris Teeter. We believe wellness should be accessible to every family in every zip code, and we’re building the infrastructure to make that possible.

Data Architect

Applicants must have permanent work authorization in the United States. FoodHealth Company is unable to provide visa sponsorship or support visa transfers for this position.

Are you a data architecture visionary ready to build the systems that will revolutionize how millions understand food health?

FoodHealth Company (formerly bitewell) is seeking a highly technical and visionary Data Architect to lead the design, implementation, and governance of our core data infrastructure. As the business evolves into one whose primary asset is a deep, extensible, and authoritative data corpus, this role is central to engineering the systems, flows, and validation regimes that will power our data ecosystem.

The ideal candidate will possess a deep command of data architecture fundamentals — including data modeling, pipeline construction, lineage tracking, observability, and schema evolution — and will bring an engineer’s mindset to problems of scale, governance, and automation. You will define how data flows, how it is structured, validated, and made trustworthy across every component of the enterprise.

About FoodHealth Company (formerly bitewell)

At FoodHealth Company, we’re building a future where every food choice is a health decision. Our proprietary FoodHealth Score is more than just a rating — it’s a new standard in food health evaluation that simplifies complex nutrition science and empowers consumers to make better food choices. Our ambition is to bring this transparency to every grocery shelf, every food brand, every drive through, every cafeteria and every dining table across America.

What You'll Do

Data Flow & Infrastructure Architecture

Design and document source-to-consumption data flows, including data ingress from external APIs, internal services, user-facing applications, and machine learning pipelines.

Architect modular, scalable data pipelines for ingestion, transformation, and loading, enabling traceability and reusability at every stage.

Define and implement data provenance protocols, capturing metadata and lineage across all processing layers.

Engineer streaming and batch processing systems using tools such as Apache Kafka, Apache Beam, or Airflow/Dbt, ensuring event ordering and idempotency.

Schema & Corpus Design

Model normalized, distributed data schemas that preserve data integrity and avoid redundancy across domains.

Lead the definition and maintenance of a canonical data model and schema registry to unify internal data representations.

Develop strategies for schema evolution that support backward/forward compatibility across dependent systems.

Validation & Observability

Define and implement data validation frameworks — including schema conformance, missingness detection, statistical anomaly detection, and referential integrity enforcement.

Design and deploy data observability platforms with SLAs around freshness, completeness, and accuracy.

Build automated audit and reconciliation processes to identify and alert on pipeline regressions or integrity violations.

Lineage, Governance & Metadata Management

Implement end-to-end data lineage tracking (both static and dynamic) across ingestion, transformations, and delivery pipelines.

Collaborate with Security and Compliance teams to enforce data governance policies, including retention, access control, and PII masking where applicable.

Establish a metadata layer (technical + business) that underpins data discoverability and semantic context for consumers.

Team Enablement & Documentation

Champion internal data standards and best practices, mentoring engineers on principled data design and pipeline development.

Write technical specifications, data contracts, lineage diagrams, and architecture documentation consumable by both engineering and business stakeholders.

Serve as a cross-functional bridge between data engineering, software, product, analytics, and machine learning teams.

Who You Are

7+ years experience in Data Architecture, Data Engineering, or a similar backend-focused discipline.

Mastery of SQL, data modeling (3NF, star/snowflake, dimensional modeling), and ETL/ELT frameworks.

Proven experience with distributed data systems (PostgreSQL, BigQuery, Snowflake, Redshift, Delta Lake, etc.).

Deep knowledge of data pipeline orchestration tools (Airflow, Dagster, Prefect) and transformation layers (dbt, Spark, Flink).

Experience building data quality systems (e.g., Great Expectations, Soda, Deequ) and schema validation engines.

Fluency in metadata and lineage tools (OpenLineage, DataHub, Amundsen, Marquez).

Expertise in observability tooling (e.g., Monte Carlo, Datadog, Grafana, OpenTelemetry for data).

Comfort with containerized environments (Docker, Kubernetes) and infrastructure-as-code (Terraform, Pulumi).

Familiarity with event-driven architecture and streaming data frameworks (Kafka, Pulsar, Kinesis).

Strong documentation and systems design skills with a bias for clarity, versioning, and reproducibility.

Added Bonus

Experience building data platforms in health, food, e-commerce, or scientific domains.

Familiarity with semantic layer modeling, data catalogs, and data mesh principles.

Experience supporting AI/ML model pipelines, including feature stores and training set lineage.

Background in managing large-scale historical data backfills, data remediation workflows, or schema refactoring in production.

Understanding of regulatory compliance frameworks (HIPAA, GDPR, CCPA) as they relate to data governance.

What Success Looks Like

All business-critical data pipelines are documented, version-controlled, observable, and traceable.

Schema changes are safe, validated, and governed across environments and teams.

Data flows and structures enable confidence scoring, downstream analytics, and ML inference without loss of provenance or integrity.

The business’s data corpus becomes a first-class system of record — authoritative, explainable, extensible.

Why FoodHealth Company?

We’re building technology that could fundamentally change how people eat and live.

Our ambition is to see the FoodHealth Score on every food product, menu, and grocery shelf in America.

Our culture values boldness, intellectual curiosity, and a hands-on approach.

We’re looking for someone who can bring their authentic self to work, engage in thought-provoking discussions, and contribute to our mission with both expertise and enthusiasm.

Additional Details

Location: San Francisco - hybrid (3 days in office)

Compensation: $150K - $160K with flexibility based on your experience and qualifications. Total compensation package includes equity & bonus and full benefits package.

Benefits: Health, Dental & Vision Insurance, Employee Assistance Program, Monthly Food Stipend, Unlimited PTO

FoodHealth Company celebrates diversity and is committed to building an inclusive environment for all. We’re an equal opportunity employer and encourage candidates from all backgrounds to apply and bring their authentic selves to our team as we work together to revolutionize how people make food choices.

Compensation Range: $140K - $160K

Full-time