Senior Analytics Engineer

Salary

$140,250 - $165,000 USD

Location

Remote, USA

Posted

Today

The Senior Analytics Engineer helps Mercer Advisors build a modern, trusted data foundation that wealth advisors, client service teams, and firm leaders can rely on. Reporting to the VP, Client Data & Analytics, you co-design and co-own the transformation and semantic layers that turn raw data from our platforms into governed, well-tested, documented datasets and metrics. This role sets the foundation for both served and self-serve analytics, so analysts and business users, including those using AI-enabled tools, can get trustworthy answers on their own. You bring software engineering discipline to analytics work — version control, automated testing, code review, and continuous integration — and help the team raise its standard for how data products are built, shipped, and maintained. This is a hands-on senior role with broad influence: you set patterns for the team, mentor other analysts and engineers, and partner with business and technology stakeholders.

Essential job functions

Design, build, and maintain dbt models on Microsoft Fabric that transform raw source data into clean, well-documented staging, intermediate, and mart layers, following consistent layering and naming conventions.
Co-own the Power BI semantic layer, including certified semantic models, shared measures, and row-level security, so key metrics are defined once and reused consistently across reports, dashboards, and self-service analysis.
Establish and enforce data quality standards by writing dbt tests, source freshness checks, and data contracts with upstream producers, and by setting up monitoring and alerting for pipeline and data issues.
Introduce and maintain software engineering practices for analytics, including Git-based workflows, pull request reviews, automated CI/CD for dbt and Fabric deployments, and separate development, test, and production environments.
Partner with wealth advisors, client service teams, finance, operations, and technology to translate business questions into reliable data models and metric definitions, and to retire duplicative or conflicting reporting.
Document models, metrics, and lineage with clear business definitions so analysts, business users, and AI-enabled tools can find, understand, and correctly use data on their own, and maintain a catalog of certified datasets and their owners.
Optimize performance and cost of transformations and semantic models through sound incremental loading, partitioning, and query design.
Work with the Information Security, Compliance, and Technology teams to apply appropriate access controls, privacy protections, and auditability to client and firm data, in line with Mercer Advisors policies and regulatory expectations.
Review the work of other team members, mentor analysts and engineers on modeling and engineering best practices, and contribute to the team's standards, templates, and onboarding materials.
Evaluate and recommend improvements to the team's tooling, architecture, and ways of working, and lead the rollout of approved changes.
Troubleshoot and resolve data issues, perform root cause analysis, and communicate impact and status clearly to technical and non-technical audiences.

Knowledge, skills, and abilities

Bachelor's degree in computer science, engineering, data science, statistics, information systems, or a related field, or equivalent practical experience.
Five or more years of experience in analytics engineering, data engineering, or business intelligence development, with a track record of delivering production data models used across a business.
Advanced SQL skills, including query optimization and dimensional modeling (facts, dimensions, slowly changing dimensions).
Hands-on experience with dbt in production, including modular model design, testing, documentation, macros, incremental models, and deployment practices.
Experience with Microsoft Fabric (Lakehouse or Warehouse, Data Factory or pipelines, OneLake) or a comparable cloud data platform such as Snowflake, Databricks, or BigQuery.
Strong Power BI experience, including semantic model design, DAX, row-level security, and governance of shared datasets; familiarity with a metrics or semantic layer approach.
Familiarity with AI-enabled analytics tools and what they need from the data layer to give reliable answers, such as governed metrics, clear definitions, and good documentation.
Working knowledge of Git, pull request workflows, and CI/CD pipelines (for example Azure DevOps or GitHub Actions).
Proficiency in Python for scripting, automation, and data validation.
Experience establishing data quality, testing, and observability practices, and a clear point of view on how to scale them.
Ability to translate ambiguous business needs into clear technical designs, and to explain tradeoffs to non-technical stakeholders and executives.
Strong written and verbal communication, and a collaborative, ownership-minded approach to working with analysts, engineers, and business partners.
Understanding of data privacy, security, and governance principles; experience with sensitive or regulated data is preferred.
Experience in wealth management, financial services, or another regulated industry is a plus, as is familiarity with CRM, portfolio accounting, or custodial data.

Work schedule

This professional role requires availability Monday through Friday, 8:00 AM – 5:00 PM.

Working conditions

Professional office environment, daytime hours, working inside, standing, and sitting, will be assigned to a workstation, no heavy lifting over 20 lbs.

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