Own the architecture, quality, and scalability of CloudFactory’s analytics layer.
Lead a team transforming raw data into clean, well-documented, and performant data models.
Champion best-in-class engineering practices (version control, testing, CI/CD).
Build ML-ready data products.
Collaborate closely with Data Engineering, Product, and Technology teams to deliver high-impact insights.
Design, build, and maintain scalable analytics data models using DBT and Snowflake.
Apply best practices in dimensional modeling (Kimball / Star Schema) and design ML-ready feature tables.
Ensure high data quality, reliability, and documentation across the analytics layer.
Lead the development of predictive data products to quantify user performance, churn risk, and customer health.
Integrate fraud analytics and anomaly detection into core data transformations.
Prepare and present compelling visualizations for stakeholders and translate analytical findings into actionable insights.
Explore emerging methodologies to improve data-driven decision-making.
Lead and mentor a team of data analysts and analytics engineers and provide technical guidance on DBT best practices.

Strong understanding of data modeling best practices and modern analytics engineering workflows.
Hands-on experience building and maintaining data pipelines using DBT, Snowflake, Python, and Fivetran.
Ability to independently perform core data science tasks, including model selection, training, evaluation, and interpretation.
Proficiency in data cleaning, preparation, and exploratory analysis.
Strong analytical thinking and problem-solving skills.
3–5+ years of experience in dimensional modeling (Kimball / Star Schema).
Proven experience leading projects end-to-end, from stakeholder problem definition to production data product.
Hands-on expertise with Snowflake, including performance optimization and query tuning.
Strong proficiency in Python, including production-level coding and statistical libraries.
Solid understanding of machine learning and statistical techniques, with the ability to assess appropriate usage and evaluation.
Experience with data transformation, modeling, and ETL pipelines.
Strong data visualization and storytelling skills for business audiences.
Experience building dashboards in Tableau or Amazon QuickSight.
Familiarity with workflow orchestration tools such as Airflow or Prefect.
Experience with ClickHouse or similar analytical databases.
Background in statistical modeling, time series analysis, machine learning, or AI.

Senior Analytics Engineer

Key skills