Senior Data Scientist – Data Architecture & Governance

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
• At least five years of professional experience in data science, analytics engineering, data engineering, or data architecture.
• Advanced proficiency in PostgreSQL and SQL.
• Strong proficiency in Python for data processing, analysis, automation, and modelling.
• Demonstrated experience designing and optimizing PostgreSQL databases.
• Strong understanding of relational database design, normalization, dimensional modelling, indexing, and query optimization.
• Experience building and maintaining production-grade ETL/ELT pipelines.
• Practical experience integrating relational databases with APIs and third-party systems.
• Working knowledge of MongoDB and other non-relational databases.
• Familiarity with Supabase and Firebase as connected or supporting systems.
• Experience working with AWS-hosted databases, applications, or data services.
• Experience establishing data-governance, lineage, access-control, and quality-management processes.
• Familiarity with version control, CI/CD, database migrations, testing, monitoring, and production deployment.
• Experience supporting BI tools, dashboards, and executive reporting environments.

• Experience in eCommerce, travel technology, warehousing, logistics, finance, or subscription businesses.
• Experience with Shopify, WMS platforms, CRM systems, accounting platforms, advertising platforms, or customer-support systems.
• Familiarity with dbt, Airflow, Dagster, Prefect, or equivalent transformation and orchestration tools.
• Experience with AWS services such as RDS, S3, Redshift, Glue, Lambda, CloudWatch, and IAM.
• Experience designing data warehouses or centralized analytical layers.
• Experience deploying and monitoring analytical or machine-learning models in production.
• Understanding of data privacy, security, auditability, and regulatory compliance.
• Experience working in an organization where systems and processes are rapidly evolving.

• Take ownership of the structure, integrity, and optimization of the company’s primary PostgreSQL database.
• Assess and document the existing architecture, database schemas, integrations, dependencies, and data flows.
• Design scalable PostgreSQL schemas, tables, views, materialized views, indexes, and analytical data models.
• Review and improve database performance through query optimization, indexing, partitioning, and appropriate data-modelling practices.
• Establish PostgreSQL as the governed source of truth for approved business entities and metrics.
• Define clear boundaries between PostgreSQL, MongoDB, Supabase, Firebase, and other connected systems.
• Reduce unnecessary duplication, fragmented datasets, and conflicting versions of business information.
• Create safe procedures for schema changes, database migrations, version control, testing, and deployment.
• Work with Engineering and DevOps to maintain database availability, monitoring, backups, recovery procedures, and performance.
• Ensure that new applications and features follow established data architecture standards.

• Design, build, and maintain reliable ETL/ELT pipelines between PostgreSQL and internal or third-party systems.
• Integrate data from MongoDB, Supabase, Firebase, APIs, eCommerce platforms, operational tools, and other business applications.
• Treat Firebase as a supporting source for application and event data, with validated information transferred into the central data environment where required.
• Develop standardized processes for data extraction, transformation, synchronization, reconciliation, and loading.
• Implement incremental data-processing and change-tracking methods where appropriate.
• Monitor pipeline failures, delayed data, schema changes, duplication, and synchronization issues.
• Ensure integrations are scalable, documented, testable, and recoverable.
• Design reusable datasets that can support reporting, business intelligence, automation, experimentation, and machine-learning use cases.

• Create and implement a company-wide data-management framework covering the complete data lifecycle.
• Establish standards for data collection, naming conventions, schema design, ownership, retention, transformation, access, and archival.
• Define clear data ownership across Finance, Operations, Marketing, Sales, Customer Support, Product, and other business verticals.
• Establish a controlled process for requesting new fields, tables, datasets, integrations, and reporting metrics.
• Develop data dictionaries, lineage documentation, architecture diagrams, system maps, and source-of-truth registers.
• Create formal procedures for schema changes, pipeline changes, metric definitions, access requests, data incidents, and recovery.
• Define validation, reconciliation, and approval processes for business-critical information.
• Establish role-based access controls in collaboration with Engineering, DevOps, and Information Security.
• Ensure that sensitive information is collected, stored, and accessed appropriately.
• Monitor compliance with established data protocols and address violations or weaknesses.

• Translate business problems into clear analytical questions, measurable outcomes, and technical requirements.
• Conduct exploratory, diagnostic, predictive, and prescriptive analysis.
• Develop forecasting, segmentation, anomaly-detection, and optimization models where they create measurable value.
• Support analysis across revenue, inventory, demand, customer behavior, subscriptions, fulfillment, marketing, and operational performance.
• Create governed analytical datasets using PostgreSQL as the primary foundation.
• Establish consistent definitions for KPIs and business metrics across departments.
• Ensure that models and analyses are explainable, reproducible, validated, and properly documented.
• Communicate the assumptions, limitations, and confidence levels associated with analytical outputs.
• Support the development of production-ready machine-learning and AI capabilities where appropriate.

• Build automated checks for data completeness, accuracy, consistency, validity, freshness, and duplication.
• Define data-quality thresholds and service-level expectations for critical datasets and pipelines.
• Identify and resolve inconsistencies between PostgreSQL, connected systems, and departmental reports.
• Implement reconciliation controls for financial, operational, inventory, customer, and revenue data.
• Lead root-cause analysis for data incidents and implement permanent corrective measures.
• Develop monitoring and alerting for pipeline failures, abnormal values, missing records, delayed data, and schema changes.
• Ensure dashboards and business reports use governed, validated, and reconciled datasets.

• Collaborate with Internal Systems, Engineering, DevOps, Finance, Operations, Marketing, Sales, Product, and executive leadership.
• Convert loosely defined business requirements into clear technical specifications and data models.
• Challenge unreliable assumptions, inconsistent definitions, and unsupported conclusions.
• Present technical findings in language that business stakeholders can understand and act upon.
• Train teams on data definitions, governance requirements, and responsible data usage.
• Manage competing priorities without compromising the integrity of critical systems.
• Communicate clearly during high-pressure incidents and time-sensitive projects.

• Strong ownership and accountability
• Structured problem-solving
• Ability to remain effective under pressure
• High attention to detail
• Strong technical and commercial judgment
• Clear documentation and communication
• Ability to challenge assumptions constructively
• Comfort working with ambiguity
• Strong stakeholder-management skills
• Ability to balance urgent delivery with long-term architectural integrity

Performance will be evaluated based on:

• Accuracy and consistency of critical business data
• PostgreSQL performance and reliability
• Reduction in duplicated and conflicting data
• Reliability and freshness of production pipelines
• Coverage of automated data-quality controls
• Completeness of data lineage and technical documentation
• Adoption of governance standards across business verticals
• Speed and quality of data-incident resolution
• Business value generated through analysis, forecasting, and optimization
• Ability to deliver under pressure without introducing unmanaged technical debt

Culture

• We're founder-led and operate with speed, direct communication, and clear accountability
• We invest in tools and management practices that help colleagues do their best work
• Our products are used by customers globally
• AI is intentionally embedded in how we work, create, and scale
• Senior leaders are expected to create structure, make decisions, and stay close to execution

Growth & Development

• Competitive pay and meaningful opportunities for performance-based advancement
• Ownership of strategy, systems, execution, and team buildout within your function
• Opportunity to build and scale a meaningful part of the business across two consumer brands
• Scope and compensation growth tied to performance, role expansion, and measurable business impact

We are proud to be an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, sex, religion, gender, marital status, national origin, genetics, disability, age, veteran status or other characteristics.