Lead Analytics Engineer

eShipping, LLC•Troy, MI

3d•Onsite

About The Position

The Lead Analytics Engineer serves as the primary analytics resource embedded within the Solutions team, bridging the gap between complex data systems and business decision-making. This role combines deep technical expertise in analytics engineering with a consultative partnership approach — translating business needs into well-structured data models, building scalable data pipelines, and equipping cross-functional stakeholders with the insights and tools they need to drive outcomes. The Lead Analytics Engineer also provides technical mentorship and guidance to peers, reviewing work for accuracy, and helping elevate the team's overall data maturity.

Requirements

Advanced proficiency in Python for data manipulation and analytics engineering, including writing clean, maintainable code to transform, validate, and operationalize complex datasets
Expert-level proficiency in SQL, including window functions, CTEs, complex joins, MERGE operations, and query execution plan analysis, with the ability to use SQL as a bridge between business needs and scalable data solutions
Familiarity with Delta Lake or comparable lakehouse technologies, including schema evolution, time travel, and medallion architecture patterns
Demonstrated ability to quickly learn new tools, platforms, and frameworks, and become productive with emerging technologies in a fast-evolving data environment
Demonstrated ability to translate complex business requirements into well-structured, scalable data models
Excellent written and verbal communication skills, with ability to explain technical concepts to non-technical stakeholders
Strong analytical and problem-solving skills with keen attention to detail
Ability to work independently with minimal oversight while exercising sound judgment
Comfortable mentoring others and providing technical guidance without direct management authority
Ability to manage multiple priorities and adapt in a fast-paced environment
Experience working with BI and visualization tools (e.g., Power BI, Apache Superset, or similar)
Familiarity with cloud data platforms such as Apache Spark, Databricks, Azure Data Lake, or comparable environments
Bachelor's degree in Computer Science, Data Science, Information Systems, Statistics, or a related field — or equivalent practical experience
5+ years of professional experience in analytics engineering, data engineering, or a senior data analyst role with significant hands-on data modeling responsibilities
Hands-on production experience with Apache Spark and PySpark
Working experience with lakehouse architectures (Apache Spark or comparable)
Track record of partnering directly with business teams (operations, finance, solutions, customer success, etc.) as a primary analytics resource
Experience mentoring or guiding peers in a technical environment

Nice To Haves

Freight, logistics, or transportation industry experience preferred

Responsibilities

Design, build, and maintain scalable data models, reusable datasets, and analytics-ready assets that support reliable reporting, self-service analysis, and downstream decision-making across the organization
Use SQL expertly to query, validate, and optimize data workflows, serving as a bridge between business questions, source systems, and scalable analytics solutions
Write and maintain Python-based data transformation logic, including production-grade PySpark pipelines, to manipulate, validate, and operationalize complex datasets at scale
Implement and manage bronze/silver/gold data modeling patterns within a Delta Lake or comparable lakehouse architecture
Partner directly with the Solutions team as an embedded analytics resource, proactively identifying opportunities to leverage data for operational improvements
Translate business requirements into technical specifications and deliver actionable insights to non-technical stakeholders
Guide other team members on analytics engineering best practices, data modeling standards, and technical approaches
Review the work of others to ensure data accuracy, consistency, and adherence to established standards
Read and tune established reporting solutions to diagnose and resolve performance issues
Collaborate with engineering, operations, finance, and customer success teams to understand evolving data needs
Evaluate, learn, and adopt new tools, platforms, and frameworks quickly, helping the team stay effective in a fast-evolving data environment
Serve as point of contact between data engineering function and Solutions team, fostering an embedded partnership
Proactively identify gaps in existing data models and reporting and recommend improvements
Contribute to the development and evolution of the organization's data strategy, including architecture decisions, tooling, and governance standards
Support the evaluation and adoption of new data technologies and platforms
Create and maintain technical documentation for data models, pipelines, and processes
Participate in code reviews and provide constructive, growth-oriented feedback to peers
Communicate project status, technical trade-offs, and data insights to both technical and non-technical audiences