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Data Architect

Data Architect

Applicants must have permanent work authorization in the United States. Bitewell 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?

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 Bitewell

Our vision is to create a world without diet-related disease. We're making this happen through our core product: The FoodHealth Score. The score is our proprietary, first-of-its-kind nutrition scoring system that combines cutting-edge science with innovative technology to give consumers insight and transparency into the healthfulness of food. We bring this to market with our partners in food retail, manufacturing, and data, which empowers customers to make healthier choices, merchandisers to stock healthier shelves and brands to make healthier products.

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

Preferred Qualifications

  • 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 Bitewell?

We're not just building another app – we're creating technology that could fundamentally change how people eat and live.

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.

Ready to help us make "FoodHealth Score" a household term? Apply now to join our team and make an impact on one of the most important health challenges of our time.

Additional Details

Location: San Francisco

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

Bitewell celebrates diversity and is committed to building an inclusive environment for all. We're an equal opportunity employer and don't discriminate on the basis of race, color, ethnicity, religion, sex, gender identity, sexual orientation, age, disability status, or any other protected characteristic. We encourage candidates from all communities to apply and bring their authentic selves to our team as we work together to revolutionize how people make food choices.

Average salary estimate

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What You Should Know About Data Architect, bitewell

Are you ready to take your career to the next level and join Bitewell as a Data Architect in San Francisco? We're on a mission to improve the way millions think about food health, and we need a tech-savvy visionary like you to help us achieve that! As a Data Architect, you’ll be at the helm of designing and implementing our core data infrastructure while ensuring it's seamless and trustworthy. You’ll dive deep into the worlds of data modeling, pipeline construction, and governance, all while fostering a culture of collaboration and innovation within our team. Imagine defining how data flows and is structured across our entire organization, working with leading-edge tools like Apache Kafka and Airflow to create scalable and efficient data systems. With your 7+ years of experience in Data Architecture, you’ll also document source-to-consumption data flows, engineer robust validations, and establish governance protocols that empower our mission. At Bitewell, we believe in enhancing lives through transparency in food choices, and you will be integral in shaping our data landscape, enabling healthier choices for consumers. Enjoy a competitive compensation package, including equity and an inclusive workplace that celebrates diversity. So, if you're passionate about data and want to make a meaningful impact on one of the most significant health challenges of our time, let’s connect and see how you can help bring the FoodHealth Score to households everywhere!

Frequently Asked Questions (FAQs) for Data Architect Role at bitewell
What are the primary responsibilities of a Data Architect at Bitewell?

As a Data Architect at Bitewell, your primary responsibilities include designing and documenting data flows, architecting scalable data pipelines, defining data validation and observability frameworks, and implementing governance protocols to ensure data integrity. You'll also work closely with cross-functional teams, including data engineering, product, and analytics, to establish internal data standards and best practices.

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What qualifications do I need to apply for the Data Architect position at Bitewell?

To apply for the Data Architect position at Bitewell, candidates must have at least 7 years of experience in Data Architecture or Engineering, mastery of SQL and various data modeling techniques, and expertise in distributed data systems. Familiarity with data pipeline orchestration tools and data quality systems is also crucial for success in this role.

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How does the Data Architect at Bitewell contribute to team collaboration?

The Data Architect at Bitewell plays a central role in fostering team collaboration by serving as a bridge between data engineering, software, product, and analytics teams. This role involves mentoring engineers on best practices in data design, developing technical specifications, and ensuring everyone is aligned with the organization's data strategy.

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What tools and technologies will I work with as a Data Architect at Bitewell?

As a Data Architect at Bitewell, you'll work with a range of cutting-edge tools and technologies, including Apache Kafka, Airflow, and various cloud data systems like PostgreSQL and Snowflake. Your expertise will help you design streaming and batch processing systems, implement data quality frameworks, and establish observability tooling that enhances our data ecosystem.

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What impact does the Data Architect role have on Bitewell's mission?

The Data Architect role has a profound impact on Bitewell's mission of reducing diet-related diseases by ensuring our data infrastructure is robust and reliable. By implementing efficient data flows and validation protocols, you will enable our FoodHealth Score technology to deliver trustworthy insights, helping consumers make informed food choices and ultimately leading to healthier lives.

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Common Interview Questions for Data Architect
Can you explain your experience with data modeling and its importance for a Data Architect?

Absolutely! Data modeling is crucial for organizing how data is structured, stored, and accessed within an organization. In my previous roles, I've developed numerous data models using techniques like star and snowflake schemas, ensuring that the data is both accessible and maintains integrity. Effective data modeling not only enhances data quality but also facilitates easier data retrieval and reporting, ultimately supporting better business decisions.

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What strategies do you use to ensure data quality in your designs?

Ensuring data quality involves implementing several validation techniques and monitoring systems. I typically establish a robust data validation framework to check for schema conformance and integrity. I also advocate for automated processes that can detect anomalies in data, which allows me to address potential issues proactively before they impact analysis or decision-making.

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How do you approach documenting data workflows and architecture?

Documenting data workflows and architecture is fundamental in promoting transparency and collaboration. I utilize tools that allow for visual representations of data flows, complemented by detailed documentation that outlines each component’s purpose and dependencies. This ensures that both technical and non-technical stakeholders can understand the architecture while enabling easier maintenance and onboarding.

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Describe your experience with data pipeline orchestration tools.

I have extensive experience with tools such as Apache Airflow and Prefect for data pipeline orchestration. These tools allow me to schedule tasks, create dependencies, and ensure that data flows happen efficiently and reliably. For instance, I've built several ETL processes using Airflow, which included error handling and retries to bolster the robustness of the pipelines.

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What methodologies do you apply for data governance and compliance?

Data governance and compliance are critical for maintaining data integrity and security. I typically implement a framework that includes defining data access policies, retention schedules, and compliance checks in alignment with regulations such as GDPR and HIPAA. Collaborating with security teams, I ensure that all data handling follows industry standards and best practices, thus addressing both customer trust and legal obligations.

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How do you keep up with emerging data technologies and trends?

I subscribe to industry publications, participate in webinars, and engage with data-centric communities to stay informed about emerging technologies and trends. I also experiment with new tools on personal projects, which helps me understand practical applications and their potential value to my work as a Data Architect.

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Can you give an example of a challenging data problem you've solved?

In a previous project, I faced a challenge with data synchronization between different systems that was leading to inconsistencies. I undertook a thorough analysis, designed a more effective ETL process using Apache Kafka for real-time updates, and implemented change data capture (CDC) strategies. This not only resolved the inconsistencies but also improved data accuracy across our systems significantly.

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What is your approach to schema evolution?

Schema evolution is a crucial aspect of scaling data architectures. My approach involves defining backward and forward compatibility in schema changes to avoid breaking existing applications. I use versioned schemas in data lakes and establish migrations that allow for smooth transitions, ensuring that consumers of the data can continue to access it without disruption.

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Why do you think observability is important in data infrastructure?

Observability is vital because it provides insights into the operation and health of the data pipelines. By implementing observability tools, I can monitor metrics such as data freshness and accuracy in real-time. This enables me to quickly identify and rectify any issues, ensuring that our data remains reliable and supporting timely decision-making for the organization.

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How do you collaborate with cross-functional teams as a Data Architect?

Collaboration with cross-functional teams is essential for success as a Data Architect. I engage with teams early in the process to understand their data needs, articulate the architecture decisions clearly, and solicit feedback. Regular meetings and documentation help maintain alignment and ensure that both engineering and business objectives are met. This collaborative approach fosters innovation and drives successful data initiatives.

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DATE POSTED
April 17, 2025

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