As an Analytics Engineer, you will be responsible for designing, developing, and maintaining scalable data products, feature stores, data models, and transformation frameworks that enable Data Scientists to efficiently build, deploy, and operationalize machine learning solutions.

You will serve as the bridge between Data Engineering and Data Science, ensuring that high-quality, trusted, and well-modeled data is readily available for analytics, experimentation, and machine learning use cases. The ideal candidate has strong experience in dbt, SQL, data modeling, Databricks, cloud platforms, and feature engineering pipelines.

WHAT YOU WILL DO:

Data Modeling & Transformation

Partner closely with Data Scientists to design, develop, and maintain datasets that support statistical analysis, machine learning models, experimentation, and business insights.
Design, build, and optimize scalable data models using dbt (Data Build Tool) following industry best practices for modularity, testing, documentation, and maintainability.
Develop and manage transformation pipelines that convert raw data into trusted, analytics-ready datasets.
Establish and maintain data lineage, governance, and documentation across analytical data assets.

Feature Store & ML Data Platform

Design, develop, and maintain feature stores and accompanying feature pipelines used for model training, feature serving, and batch inference.
Implement reusable feature engineering frameworks that accelerate machine learning development and improve feature consistency.
Collaborate with Data Scientists and Machine Learning Engineers to ensure feature reusability and production readiness.
Support the design and maintenance of real-time and batch feature pipelines.

Data Quality, Governance & Reliability

Implement automated data validation, monitoring, and testing frameworks to ensure data quality and reliability.
Establish data quality checks, SLAs, observability metrics, and monitoring processes across critical datasets and pipelines.
Partner closely with Data Engineering and Data Governance teams to improve data maturity, accessibility, and trustworthiness.
Serve as a subject matter expert across assigned business and data domains.

Platform Engineering & Best Practices

Build CI/CD pipelines that accelerate deployment and proactively detect issues before production release.
Create and maintain reusable software packages, tools, and frameworks that improve Data Science productivity.
Establish best practices for data modeling, transformation, testing, deployment, and architecture.
Conduct code reviews and architectural reviews to ensure adherence to engineering standards.
Communicate technical architectures, trade-offs, and solutions to both technical and non-technical stakeholders.
Maintain architecture diagrams, technical documentation, and platform standards.

WHAT WE'RE LOOKING FOR:

Bachelor's degree in Computer Science, Information Technology, Statistics, Engineering, Mathematics, or a related quantitative discipline.
At least 4+ years of experience in Analytics Engineering, Data Engineering, or related fields.
At least 2+ years of hands-on experience with dbt (Data Build Tool) in a production environment.
Strong expertise in SQL, data modeling, and ETL/ELT development.
Experience with Databricks, Apache Spark, or modern Lakehouse platforms.
Experience working with cloud technologies such as AWS, Azure, or GCP.
Experience building and maintaining CI/CD pipelines and automated testing frameworks.
Experience collaborating with Data Science, Data Engineering, and business stakeholders.
Experience working with large-scale datasets and complex data ecosystems.

Senior Analytics Engineer