Position Summary
The Data, Analytics, and AI team runs the company data warehouse, the central database that brings together information from NetSuite, the online store, and the shipping partner, and turns it into reports, metrics, and tools the business can trust. The Manager leads that distributed team: sets its direction, decides the future of the data warehouse (two data warehouse efforts exist today and the company needs one), and builds the team as the work grows. The Manager owns every report that combines data from more than one system and the standard for how key numbers are defined and checked. The Manager also owns the automation and AI tools built on that data.
Key Responsibilities
Percentages are the approximate share of time over a year.
- Decide the data warehouse platform and consolidate it (20%). Two data warehouse efforts exist today. Evaluate both with the Senior Data Engineer, decide whether and how to combine them, and own the resulting platform: architecture, cost, security, and the automated feeds from NetSuite, the online store, and the shipping partner.
- Own cross-system reporting and metric standards (20%). Any report that mixes NetSuite data with data from another system belongs to this team. Define how each key metric (revenue, margin, cost, inventory, channel profitability) is calculated and tested so every report uses the same definition. Own the monthly product data quality report delivered to Enterprise Systems and the monthly comparison of online store products against NetSuite.
- Set the team's direction and priorities (15%). Keep one backlog of data, reporting, and AI requests from leadership, Merchandising, Marketing, Finance, Operations, and Digital Commerce. Rank the work and make sure the team delivers the highest-value items first.
- Build and lead the team (15%). Manage the Senior Data Engineer, the Analytics Engineer, and the BI Analyst, Commercial. Hire for open seats, set priorities, review work, run performance reviews, and take over the interim technical lead duties the Senior Data Engineer carries today.
- Deliver automation and AI tools on the warehouse (15%). Identify where automation or AI on top of trusted data removes manual work or improves decisions. Pilot, measure, and deploy those tools with clear ownership and guardrails for data access and accuracy.
- Govern data dependencies on upstream systems (15%). The warehouse reads from NetSuite and the online store. Make sure every upstream change that alters how data is stored is reviewed by this team before it goes live, follow the integration standards set by the Technical Lead: ERP, and keep daily loads and quality checks running with backup coverage.
Qualifications
Education:
Bachelor’s degree in computer science, Statistics, Mathematics, Information Systems, Engineering, or a related field, or equivalent work experience.
Experience:
- Seven or more years in data engineering, analytics engineering, or business intelligence, including three or more years leading a data team.
- Built or ran a cloud data warehouse in production, including the pipelines that feed it and the tests that protect it.
- Delivered reporting for a retail, ecommerce, consumer products, or manufacturing business.
- Shipped at least one automation or AI application on top of a data platform and measured its results.
- Preferred: NetSuite and Shopify as source systems; consolidation or migration of a data platform.
Knowledge:
- Dimensional and analytical data modeling, ELT pipeline design, and orchestration.
- SQL at an expert level, and at least one cloud warehouse such as BigQuery, Snowflake, Redshift, or Databricks.
- Data transformation and testing frameworks (dbt or similar) and data quality monitoring.
- BI tools such as Looker, Power BI, or Tableau, and the design of a metric layer with one definition per measure.
- Large language model and automation patterns for business use, including their limits, cost, and data access controls.
- ERP, ecommerce, and logistics data structures: orders, items, bills of materials, costs, inventory, shipments, and marketplace settlements.
- Data governance, access control, and privacy basics for customer and financial data.
Skills:
- Making and defending a platform decision with cost, risk, and migration effort laid out plainly.
- Turning vague business questions into defined metrics and reliable data sets.
- Reading and reviewing pipeline and transformation code written by the team.
- Managing a small distributed team across time zones, including remote staff.
- Plain written and spoken communication with executives, Merchandising, Marketing, and Finance.
Decision making:
Decides which data platform the company runs on, how key company metrics are defined, what the team works on and in what order, and which automation and AI tools are built. Numbers produced by this team are used for channel profitability, inventory, and financial decisions, so errors in definition or data quality reach leadership directly.
Problem Solving:
Typical problems: two reports that disagree because they use different definitions; a NetSuite or online store change that breaks a feed; a question (such as profit by sales channel) that needs product cost, store, and shipping data joined correctly; a request for an AI tool where the underlying data is not yet trustworthy; limited team capacity against a long request list.
Physical Activity / Environmental demands:
Office and computer work: prolonged sitting, keyboard and screen use, and video and phone meetings. Travel averages once per quarter, to the Portland office, the manufacturing operation in Mexico, or US retail locations.
Language Skills:
English: fluent in speaking, reading, and writing. Required for all essential functions of the position.
Employees must reside in a state where the company is currently registered to conduct business and process payroll. At this time, we are able to hire in the following states: Oregon, Texas, Washington, Florida, Virginia, South Carolina, Nevada, Illinois, Michigan, Ohio, Colorado, Tennessee, New Hampshire and Georgia.
What we can offer you!
- Health, dental & vision insurance (75% cost covered by PLG for team member)
- 401(k) with 4% company match
- Discounted pet insurance
- Holidays (including 1 floating holiday), vacation & sick time
- Store discount
- Dynamic & collaborative team environment
About Portland Leather
We’ve come a long way since the garage days. Portland Leather Goods was founded in 2016 to solve a simple problem: Why was it so hard to find an affordable, full-grain leather bag that actually looked and felt great? We’ve grown fast by staying focused — on quality, on value, and on creating a brand people actually love to be part of. Now, we’re building something even bigger. And we’re doing it with people who love taking ownership, bringing energy, and making a real impact. If that’s you — we’d love to meet you.
At Portland Leather Goods, we are proud to be an Equal Opportunity Employer.
We celebrate diversity and are committed to creating an inclusive environment for all team members. We welcome applicants from all backgrounds to join our team.
Check us out at portlandleathergoods.com and on Instagram @portlandleather
Skills Required
- Bachelor's degree in computer science, statistics, mathematics, information systems, engineering, or a related field, or equivalent work experience.
- Seven or more years of experience in data engineering, analytics engineering, or business intelligence.
- Three or more years leading a data team.
- Experience building or running a cloud data warehouse in production, including data pipelines and testing.
- Experience delivering reporting for a retail, ecommerce, consumer products, or manufacturing business.
- Experience shipping at least one automation or AI application on a data platform and measuring its results.
- Experience with NetSuite and Shopify as source systems.
- Experience consolidating or migrating a data platform.
- Knowledge of dimensional and analytical data modeling, ELT pipeline design, and orchestration.
- Expert-level SQL skills and experience with at least one cloud data warehouse.
- Experience with data transformation and testing frameworks such as dbt and data quality monitoring.
- Experience with BI tools such as Looker, Power BI, or Tableau, and metric-layer design.
- Knowledge of large language model and automation patterns, including limitations, cost, and data access controls.
- Knowledge of ERP, ecommerce, and logistics data structures.
- Knowledge of data governance, access control, and privacy basics for customer and financial data.
- Ability to manage a small distributed team across time zones, including remote staff.
- Fluent English speaking, reading, and writing skills.
- Residence in an eligible state where the company is registered to conduct business and process payroll.
What We Do
Founded by Curtis Matsko around 2015-2016, Portland Leather Goods specializes in crafting high-quality, simply designed, and affordable full-grain leather bags and accessories. Based in Portland, Oregon, the company's mission is to make premium leather accessible to everyone, offering handmade products such as tote bags and journals that are delivered to a global community of leather lovers.
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