Senior Analytics Engineer

Responsibilities

• Analyze inventory trends and generate reports to drive decision-making and support supply chain optimization.
• Maintain optimal inventory levels across the supply chain network, balancing service levels with working capital targets.
• Collaborate with demand planners and buyers to align inventory strategies with sales forecasts and promotional plans.
• Lead root cause analysis for inventory discrepancies, stockouts, overstock, and slow-moving items.
• Identify and implement process improvements related to inventory planning, replenishment, and warehouse efficiency.
• Develop KPIs and dashboards to track inventory accuracy, turns, cycle times, and other performance metrics.
• Support inventory reconciliation and cycle count programs in coordination with warehouse and finance teams.
• Assist with ERP system optimization, particularly related to inventory modules, safety stock parameters, and reorder points.
• Provide insights into inventory risk and obsolescence to support product lifecycle management.

Requirements

• 5+ years of experience in Analytics Engineering, Data Engineering, or Business Intelligence, with ownership of production analytics systems.
• 5+ years of hands-on experience modeling analytics-ready data using dbt with SQL and/or Python.
• Expert-level SQL, including writing, optimizing, and debugging complex analytical queries.
• Deep experience querying MPP analytical databases such as StarRocks, Snowflake, BigQuery, or Redshift.
• Strong experience with one or more modern BI tools (e.g., Omni, Tableau, Looker, Power BI), including building semantic models, reusable metrics, and executive dashboards to enable consistent, self-serve analytics.
• Hands-on experience orchestrating data pipelines with Airflow, Dagster, or equivalent.
• Working proficiency in at least one scripting language (e.g., Python, Bash, R, or Julia).
• Proven ability to translate complex data into trusted models, metrics, and visualizations used by senior stakeholders.

Preferred Experience

• Built and operated production data pipelines or data platforms, with strong software engineering practices (testing, CI/CD, code review).
• Deep hands-on experience with stream processing and transport systems (e.g., Flink, Kafka), including performance tuning and failure debugging.
• Familiarity with open table formats such as Iceberg, Hudi, Delta, or Paimon, including an understanding of how they impact data modeling, reliability, and analytics performance.