sweedpos.com - Analytics/Data Engineer
Requirements
• 5+ years of experience in analytics engineering, data engineering, BI development, or similar data-focused roles • Solid understanding of analytical data modeling: facts, dimensions, grains, SCD patterns, data marts • Understanding of ETL/ELT pipelines and reporting layers • Experience with query optimization, partitioning, incremental models, and data pipeline reliability • Experience with data reconciliation and investigating inconsistent metrics • Ability to work with business requirements and ask strong clarifying questions • Good written and spoken English • Strong ownership mindset and ability to work autonomously in a fast-changing environment • Willingness to learn, adapt, and dive deeper into technical details • Experience with Metabase, Superset, Cube.dev, Looker, Tableau, or similar BI / semantic layer tools • Experience with retail, marketplace, eCommerce, fintech, payments, or transactional data • Experience working in distributed / remote teams • What Else Matters • This role is best suited for someone who enjoys the middle ground between analytics and engineering. • You probably won’t enjoy this role if you only want to build dashboards or only want to work on pure infrastructure. We need someone who can take a requirement, understand the business logic behind it, design the model, implement it in dbt, validate the result, and ensure the reporting layer can actually be trusted. • The environment is still evolving, so we value people who are comfortable with ambiguity, changing priorities, and imperfect processes.
Responsibilities
• Build and maintain analytics data models using dbt - incremental pipelines (merge strategies, hashdiff, SCD Type 1/2) across retail domains (sales, inventory, loyalty, marketing, promotions), with strong emphasis on structure, documentation, and maintainability • Implement data quality tests and validation logic, ensuring accuracy and trust across reporting layers • Own conformed dimensions as shared contracts across downstream consumers • Collaborate with the Data Architect to apply consistent modeling standards and support architecture evolution • Work with internal teams and sometimes clients to clarify requirements and align on metric logic • Translate business needs into robust, reusable data models • Ensure the integrity of client-facing reports, including reliability, freshness, and metric correctness • Contribute to clear documentation, metric definitions, and data contracts • Support the continuous improvement of our modern data stack: dbt, Trino, ClickHouse, Airflow, Cube.dev, Metabase
Benefits
• Our customers - cannabis retailers - rely on data to make daily business decisions. As an Analytics Engineer, your work will directly power these decisions via clean, performant, and well-governed data models. • You’ll be responsible for transforming raw data into reliable reporting layers — working closely with our Data Architect, engineers, product analysts, and sometimes even clients. You’ll also play a key role in enforcing data quality through testing and validation practices. • WHAT TO DO IN THE PROJECT? • Build and maintain analytics data models using dbt - incremental pipelines (merge strategies, hashdiff, SCD Type 1/2) across retail domains (sales, inventory, loyalty, marketing, promotions), with strong emphasis on structure, documentation, and maintainability • Implement data quality tests and validation logic, ensuring accuracy and trust across reporting layers • Own conformed dimensions as shared contracts across downstream consumers • Collaborate with the Data Architect to apply consistent modeling standards and support architecture evolution • Work with internal teams and sometimes clients to clarify requirements and align on metric logic • Translate business needs into robust, reusable data models • Ensure the integrity of client-facing reports, including reliability, freshness, and metric correctness • Contribute to clear documentation, metric definitions, and data contracts • Support the continuous improvement of our modern data stack: dbt, Trino, ClickHouse, Airflow, Cube.dev http://Cube.dev, Metabase • WHAT PROFESSIONAL SKILLS ARE IMPORTANT FOR US? • 5+ years of experience in analytics engineering, data engineering, or BI development • Strong SQL skills and hands-on experience with dbt • Solid understanding of data modeling for analytics/reporting, including fact/dimension and SCD patterns design • Experience writing and maintaining data quality tests (e.g. dbt tests, custom SQL assertions, test coverage frameworks) • Experience with modern cloud-based data warehouses (e.g. Snowflake, ClickHouse, Redshift, BigQuery) • Excellent spoken and written English — you’ll communicate with internal teams and sometimes with external clients • Grain fluency - instinct for when a join will fan out, double-count, or drop rows • Reconciliation thinking - can trace a wrong mart number back to its source • Metric definition - translates ambiguous asks into precise, defensible definitions • Ability to clearly explain data logic and metric definitions to non-technical stakeholders • Meticulous approach to documentation, testing, and ownership of data artifacts • Trino / Presto specifically • Semantic layer experience (Cube.dev http://Cube.dev, dbt Semantic Layer, LookML) • Experience supporting client-facing dashboards or embedded analytics • Multi-tenant data warehouse experience • WHAT ELSE MATTERS? • Proactivity – We love team members who take initiative and provide feedback • Critical thinking – We value problem-solvers who think beyond just writing code • Adaptability – Our industry is evolving fast, and we need people who thrive in change • Salary in USD (B2B contract with the US company) • 100% remote – We’re a remote-first company, no offices needed! • Flexible working hours – Core team time: 09:00-15:00 GMT (flexible per team) • 20 paid vacation days per year • 12 holidays per year • 3 sick leave days • Medical insurance after probation • Equipment reimbursement (laptops, monitors, etc.) • 1. Recruiter Call (up to 45 minutes) – Intro & expectations • 2. Technical Interview (up to 1.5 hours) – SQL, dbt, data modeling, and DQ test logic • 3. Final Interview (up to 30 minutes)
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