Senior Analytics Engineer

Gerber Childrenswear•Greenville, SC

About The Position

At Gerber Childrenswear, we stand for quality, everyday comfort, and safety. From the clothes we design, to the fabrics we select, to our focus on sustainability and community, we have got what baby needs for their first 1000 days and from one generation to the next. Our mission is to be a trusted partner to those all responsible for the parenting journey. We do this by seeking every day to answer the question - Will this be safe and bring comfort and joy to every child? We are dedicated to our mission and passionate about collaboration, moving quickly, breaking down barriers, leveraging technology, eliminating bureaucracy and constantly striving to evolve. Our cultural foundation is built on quality, trust, and value, all bundled together in 90 years of happy little clients while focused on continuously improving to ensure many more years of happy little clients. We are seeking an experienced and highly skilled Senior Analytics Engineer to join our analytics team. In this role, you will serve as a key technical partner responsible for designing and delivering scalable data solutions that drive decision-making across Sales, Finance, and Operations. You will work across the full business lifecycle—including financial performance, revenue and pricing, demand forecasting, inventory management, and operational execution—to ensure data is transformed into actionable insights that improve efficiency, profitability, and strategic alignment. You will partner closely with business analysts, business intelligence, and cross-functional stakeholders to build reliable data products, enhance data quality, and embed analytics into core business processes.

Requirements

Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Business Analytics, or a related field, or equivalent practical experience
3–5 years of experience in data engineering, analytics, or a related field
Experience working with APIs, data ingestion, and ETL/ELT pipelines
Experience building dashboards and reports using BI tools (e.g., Tableau, Power BI)
Experience working with business data across financial, sales, or operational domains
Strong proficiency in SQL and/or Python for data analysis and engineering
Experience with modern data platforms and tools such as Databricks (e.g., Spark SQL, Delta Lake)
Familiarity with data modeling concepts, including star/snowflake schema and dimensional modeling
Experience with BI tools and data visualization platforms
Proficiency with version control and deployment practices (e.g., Git, CI/CD pipelines)
Strong analytical and problem-solving skills with attention to detail
Ability to translate business requirements into scalable data solutions and actionable insights
Effective communication skills with the ability to collaborate with both technical and non-technical stakeholders
Highly organized with the ability to manage multiple priorities and meet deadlines
Self-directed and able to work independently while contributing to a collaborative team environment

Responsibilities

Lead the design, development, and optimization of scalable data pipelines and analytical frameworks across Financial, Logistics, and Sales domains
Write and optimize advanced SQL queries and Python scripts for complex data extraction, transformation, and analysis
Design and maintain end-to-end data ingestion workflows integrating data from internal and third-party sources via APIs and modern ELT/ETL methods
Build, manage, and continuously improve enterprise-grade dashboards and reports using Tableau, Power BI, Databricks or comparable platforms
Own and manage data workflows within Databricks, including notebooks, Delta Lake, Spark SQL, and job orchestration
Serve as a subject matter expert and technical mentor for junior and mid-level analysts
Partner with business stakeholders to define KPIs, translate strategic requirements into analytical solutions, and present findings to leadership
Establish and enforce data quality standards, documentation practices, and governance policies across the analytics function
Proactively identify opportunities to improve data infrastructure, reporting efficiency, and analytical maturity
Establish and enforce data quality standards, documentation practices, and governance policies across datasets and reporting layers