Job Description

Purpose

We are seeking a collaborative, business-oriented Analytics Engineer to join our growing Data and Analytics team, sitting at the intersection of data engineering, data modeling, business intelligence, and business partnership.

The ideal candidate brings hands-on expertise in Snowflake, SQL, dbt, and Python, and is comfortable applying AI-powered tools such as Snowflake Cortex and Microsoft Fabric Data Agents to help the organization move beyond traditional reporting toward AI-enabled, self-service, and predictive analytics.

Key Accountabilities

Design, build, and optimize scalable data models and analytics-ready datasets in Snowflake, including SQL transformations, stored procedures, views, tasks, and streams.

Build and maintain Git-backed dbt projects (native on Snowflake) with staging/intermediate/mart layering, reusable macros, testing, and Snowflake Task-based orchestration.

Build and support AI-powered analytics experiences using Snowflake Cortex (semantic views, Cortex Analyst/Search) and Microsoft Fabric Data Agents, enabling natural-language and conversational access to governed datasets.

Use Snowflake Notebooks and Python for data exploration, automation, prototyping, and advanced analytics enablement.

Partner with business stakeholders to translate reporting and analytical requirements into technical specifications and reusable data products.

Collaborate with Data Engineering on ingestion, transformation, and orchestration across Azure Data Factory, Azure Data Lake Storage, and Snowflake.

Design and document enterprise data models, including dimensional models, curated datasets, metric definitions, and data lineage.

Support trusted semantic layers and analytical datasets used by Power BI dashboards, executive reporting, and business self-service analytics.

Apply strong data quality practices, including reconciliation, validation, and root-cause analysis for data issues.

Document key datasets and metrics, and present insights, recommendations, and risks in business-friendly terms.

Qualifications

Education:

Undergraduate degree in Computer Science, Engineering, Mathematics, Statistics, Business Analytics, Data Science, or a related field.

Experience:

3–5 years of hands-on experience in data engineering, analytics engineering, data modeling, or business intelligence.

Skills & Abilities:

Intermediate to advanced-level hands-on experience with Snowflake, including warehouse concepts, SQL development, performance tuning, and production support.

Strong proficiency in SQL, including complex joins, CTEs, window functions, and stored procedures.

Practical experience with Python for data processing, automation, or analytics support.

Experience building or supporting data pipelines using Azure Data Factory and Azure Data Lake Storage.

Working knowledge of data modeling concepts, including dimensional modeling, slowly changing dimensions, and curated data layers.

Hands-on experience building production dbt projects, including model layering, macros, testing, and Git-based deployment.

Experience supporting BI solutions, preferably with Power BI, and the ability to work directly with business stakeholders and communicate clearly with non-technical audiences.

Strong problem-solving skills and proven ability to collaborate effectively within a close-knit, fast-paced team.

Snowflake certification, such as SnowPro Core, strongly preferred.

Experience with Snowflake Notebooks, Snowpark, or Python-based analytical workflows.

Exposure to Snowflake Cortex (Cortex Analyst, Cortex Search) or Microsoft Fabric Data Agents for AI-powered, conversational analytics.

dbt Certified Associate or equivalent hands-on dbt experience with custom macros and orchestration patterns.

Experience with Git/GitHub, DevOps practices, and CI/CD deployment controls.

Exposure to predictive analytics, forecasting, or data science workflows; experience in senior living, healthcare, or multi-site operations is an asset.

Experience creating executive-ready presentations, and familiarity with data governance, metadata management, and lineage practices.

Snowflake & AI-Enabled Analytics: Ability to design and troubleshoot Snowflake solutions, and apply AI tools such as Cortex or Fabric Data Agents to extend self-service analytics.

Analytics Engineering Mindset: Ability to create reusable, governed datasets using tools such as dbt.

Business Partnership & Communication: Comfortable working directly with stakeholders and explaining technical work clearly to non-technical audiences.

Data Quality & Problem Solving: Strong commitment to accuracy and ability to investigate issues and recommend scalable solutions.

Ownership & Collaboration: Self-driven and a strong team player who manages multiple priorities in a fast-moving environment.

Effort

Requires sustained concentration for data validation, reconciliations, and journal entry preparation.

Involves extended periods of computer use and handling large datasets.

Requires strong focus on accuracy, attention to detail, and meeting deadlines.

Working Conditions

Work is generally performed in an office environment

At Chartwell, we’re all about Making People’s Lives BETTER: the lives of our residents and their families, and the lives of our employees. Join an exceptional group of diverse, inspiring, and caring people who are empowered to provide personalized, human experiences for our residents and staff through the connections they make every day within our communities.

Chartwell’s commitment to diversity and inclusivity is a commitment to hiring people whose skills and abilities contribute the most to the success of the organization and who reflect the communities in which we live and work. We are an equal opportunity employer and welcome applications from a wide range of qualified candidates, including people with disabilities. If you have questions or require assistance with the application process, please email accessibility@chartwell.com or call 1-888-663-6448.

Chartwell may use artificial intelligence to assist in screening and assessing applicants for this position.

We thank all applicants for their interest, however, only those selected for further consideration will be contacted.