Join a team focused on analytics enablement-designing analytics-ready data models and a trusted semantic layer that standardizes business metrics. You will partner with stakeholders to translate requirements into well-modeled datasets in Databricks and Snowflake, using SQL as your primary language and Python when needed. You will define consistent metrics and semantic conventions that enable scalable self-service reporting. You will help create a data ecosystem where consumers trust and reuse what has been built.
Job summaryAs an
Analytics Solutions Associate within
Analytics Engineering, you will build analytics-ready data models and a trusted semantic layer that standardizes business metrics. You will partner with stakeholders to translate requirements into governed, reusable datasets and semantic definitions. You will own the structure and usability of downstream analytics by defining grains, dimensions, facts, conformed entities, and metric logic so teams can move faster with confidence. You will collaborate with upstream engineering partners to ensure source-to-model alignment and ensure data quality and documentation meet a high bar. Success looks like consistent key performance indicator definitions across dashboards, clear semantic conventions, and performant, well-documented models.
Job responsibilities- Lead development of analytics data models (dimensional and/or domain-oriented) optimized for reporting, business intelligence, and self-service consumption.
- Design and maintain a semantic layer (standardized metrics, dimensions, entities, and business definitions) to ensure consistency across dashboards and analyses.
- Translate stakeholder requirements into clear modeling deliverables (entities, grains, metric definitions, acceptance criteria).
- Build transformations primarily in SQL, leveraging Python when needed for complex logic, automation, or validation.
- Implement and champion data quality controls (tests, reconciliations, anomaly checks) tied to business-critical metrics.
- Optimize model performance in Snowflake and/or Databricks (efficient joins, partitioning and clustering strategies where applicable, cost/performance trade-offs).
- Collaborate with upstream teams on source system understanding (including semi-structured data and non-relational sources) and ensure analytics models reflect correct business meaning.
- Establish modeling standards: naming conventions, documentation, lineage, metric governance, and change management for semantic definitions.
- Support enablement by documenting curated datasets, creating user guidance, and helping consumers adopt the semantic layer correctly.
Required qualifications, capabilities, and skillsMinimum education and experience required: Master's degree in Information Technology, Computer Science, Management Information Systems, Operations Research or related field of study plus 3 years of experience in the job offered or as Analytics Engineer or related occupation. The employer will alternatively accept a Bachelor's degree in Information Technology, Computer Science, Management Information Systems, Operations Research or related field of study plus 5 years of experience in the job offered or as Analytics Engineer or related occupation.
- Advanced SQL skills (complex joins, performance tuning, incremental logic).
- Strong understanding of data modeling (facts and dimensions, grains, conformed dimensions, slowly changing dimensions, metric design).
- Demonstrated experience building or operating a semantic layer or metrics framework (tool-agnostic; ability to standardize key performance indicator logic and definitions).
- Comfort working with semi-structured data (JSON) and non-relational sources and modeling them for analytics.
- Exposure to data governance concepts (role-based access control, data classification, lineage, audit requirements).
- Working experience with Snowflake and/or Databricks in an analytics context.
- Practical Python skills for data workflows (validation, automation, notebooks and scripts).
- Ability to partner with stakeholders, clarify ambiguous requirements, and drive to measurable outcomes.
- Strong documentation habits and attention to data correctness.
Preferred qualifications, capabilities, and skills- Experience with testing and documentation.
- Familiarity with business intelligence tooling and semantic consumption patterns (for example, Tableau, Sigma, or Looker concepts).
- Knowledge of orchestration and observability (for example, Airflow, Dagster, or Azure Data Factory; logging and alerting; service level agreement mindset).
ABOUT USJPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
ABOUT THE TEAMJ.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.
