Software Developer & Data Engineer at Wisecode

Job description

Build the food intelligence layer at WISEcode. Most people have no real way to know what's in their food or what it does to them. The information is scattered, inconsistent, and written to sell rather than to inform. WISEcode exists to change that: to give people a source they can actually trust, and the freedom to decide for themselves once they have it. Getting there takes more than a better app. It takes a system underneath that turns fragmented food data into answers that hold up: consistent, explainable, and the same whether they reach a parent scanning a label, a brand seeking verification, or an institution making decisions at scale. That system is what we are building, and it is the reason the work compounds instead of resetting with every new product. The Role We are hiring a Software Developer & Engineer to work as a senior individual contributor, serving as the second engineer on The Pantry, our new data lakehouse (DuckLake on S3 Parquet, Postgres catalog, Prefect orchestration, Python throughout) . You will work alongside our Data team, combining hands-on implementation with platform and data architecture design in an early-stage environment where the shape of the work is still yours to define. You bring 3+ years of experience in a similar role, along with real comfort with ambiguity and pace. What you get in return is a problem that matters and the room to shape how it gets solved. Key Responsibilities Lakehouse Architecture & Implementation: Own end-to-end implementation and collaborate on data/platform architecture alongside our data team. Source Ingestion: Build and establish patterns for a variety of sources including APIs, text from public sources, and external databases. Identity & Entity Resolution: Merge the many records we collect for each product, across retailers and dates, into one trustworthy published record. Decide which source wins on each disagreement, and keep every original record so each published value can be traced to its source. Pipeline Consolidation & Enrichment: Consolidate existing food enrichment pipelines down to a single pipeline in collaboration with our engineering team. Downstream Connectors: Build robust connectors to feed collapsed food data to WISE Intel and the consumer app. Operational Discipline & Configuration: Maintain strict fail-fast config discipline (magic values in config files; early loud failures for missing settings). Data Quality & Reporting: Establish ingest capabilities and quality reporting for Data Assembly readiness. Technical Documentation: Write plain, short, dated decision records prior to code implementation. Technical Skills Languages: Native proficiency in Python and SQL. Storage & Query Engines: DuckDB, DuckLake, S3 Parquet. Databases: Postgres, DynamoDB (state management). Orchestration & Frameworks: Prefect, Kubernetes/Fargate job runners; familiarity with dbt, Dagster, or Airflow. AWS Infrastructure: S3, ECS, Lambda, EventBridge, SNS, Terraform, IAM. Reporting & Data Modeling: Lakehouse, analytic/dimension design; Quarto reporting; Medallion architecture (bronze/clean), star schema (food sighting, ingredient, nutrient, provenance). CI/CD: github actions Minimum Qualifications 3+ years of experience in a similar role Proven track record of designing and shipping a lakehouse or data warehouse from scratch, and operating it for at least one year in production. Demonstrated experience treating data identity and entity resolution as first-class architectural questions. Experience running batch data pipelines that invoke paid cloud/third-party APIs at scale with strict cost efficiency and budget controls. Experience pairing daily with other engineers in a fast-paced environment. Preferred Qualifications Experience making architectural trade-offs between lightweight/embedded query engines (e.g., DuckDB) versus distributed systems (Spark, Databricks, Snowflake). Familiarity with data acquisition methods (e.g., web scraping, OCR API integration). Note: Food or nutrition domain knowledge, deep Spark/Databricks/Snowflake experience, and people management experience are explicitly NOT required for this role. Compensation Competitive base, bonus, and meaningful equity. Details shared in the hiring process. Benefits Location: Fully remote Unlimited PTO & Paid Holidays: Unlimited time off and 10 paid holidays Financial Wellness: 401(k) with a 4% company match, immediately vested Health Benefits: Comprehensive medical, dental, vision, life, and additional ancillary coverage. 100% coverage for employees, with 80% covered for dependents 100% employer paid STD, LTD, Identity Theft, and Life insurance FSA and HSA plans available, for our HSA: $200/mo contributions for Employee plans $400/mo contributions for Employee + Dependent plans Why WISEcode We are building the layer that food decisions will run on. That is quiet, compounding work: broadening coverage, sharpening precision, and making every output repeatable and defensible. We value transparency, rigor, and respect for the person making the decision, who deserves clear information and the freedom to reach their own conclusion. Come in early enough and you help shape how this system works instead of operating inside one someone else already designed. Research shows that some groups hesitate to apply unless they meet every qualification. If you’re excited about this role but don’t check every box, we encourage you to apply. At WISEcode, we value diverse experiences, transferable skills, and the unique strengths each person brings. WISEcode is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Requirements

Must-haves

Build the food intelligence layer at WISEcode.
You bring 3+ years of experience in a similar role, along with real comfort with ambiguity and pace. What you get in return is a problem that matters and the room to shape how it gets solved.
Lakehouse Architecture & Implementation: Own end-to-end implementation and collaborate on data/platform architecture alongside our data team.
Source Ingestion: Build and establish patterns for a variety of sources including APIs, text from public sources, and external databases.
Pipeline Consolidation & Enrichment: Consolidate existing food enrichment pipelines down to a single pipeline in collaboration with our engineering team.
Downstream Connectors: Build robust connectors to feed collapsed food data to WISE Intel and the consumer app.
Operational Discipline & Configuration: Maintain strict fail-fast config discipline (magic values in config files; early loud failures for missing settings).
Data Quality & Reporting: Establish ingest capabilities and quality reporting for Data Assembly readiness.
Technical Documentation: Write plain, short, dated decision records prior to code implementation.
Technical Skills
Languages: Native proficiency in Python and SQL.
Storage & Query Engines: DuckDB, DuckLake, S3 Parquet.
Databases: Postgres, DynamoDB (state management).
Orchestration & Frameworks: Prefect, Kubernetes/Fargate job runners; familiarity with dbt, Dagster, or Airflow.
AWS Infrastructure: S3, ECS, Lambda, EventBridge, SNS, Terraform, IAM.
Reporting & Data Modeling: Lakehouse, analytic/dimension design; Quarto reporting; Medallion architecture (bronze/clean), star schema (food sighting, ingredient, nutrient, provenance).
CI/CD: github actions
3+ years of experience in a similar role
Proven track record of designing and shipping a lakehouse or data warehouse from scratch, and operating it for at least one year in production.
Demonstrated experience treating data identity and entity resolution as first-class architectural questions.
Experience running batch data pipelines that invoke paid cloud/third-party APIs at scale with strict cost efficiency and budget controls.
Experience pairing daily with other engineers in a fast-paced environment.

Nice-to-haves

Experience making architectural trade-offs between lightweight/embedded query engines (e.g., DuckDB) versus distributed systems (Spark, Databricks, Snowflake).
Familiarity with data acquisition methods (e.g., web scraping, OCR API integration).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

postgrespythonsqlkubernetesawsci/cdapi

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Wellness