Grasshopper Bank

Customer Analytics Engineer

New York, Remote

Actively hiring, first seen by us 5 days ago

Pay: Pay not listedSimilar roles state $122K–$190K
Work setting: Remote — work setting source
Listed location: New York, Remote
Read the full posting
Employment: Full-time — employment source
Employment type Full Time
Employment type in the employer’s posting: full_time
From the employer’s posting

What you’ll work on

Full posting

Design dimensional models and OBT datasets in BigQuery for a bank-wide customer master
Build and maintain ELT/ETL pipelines for customer interaction and core banking data
Sync enriched customer data into Braze and CRM systems via reverse-ETL
Design and maintain executive Tableau dashboards on acquisition, retention and churn
Implement automated data quality testing and anomaly detection across customer pipelines

From the employer’s posting

Design, build, and optimize scalable dimensional models (Kimball methodology) and One Big Table (OBT) datasets in Google BigQuery

Design, deploy, and maintain resilient ELT/ETL pipelines (using SQL, Python, and orchestration tools)

Engineer reliable pipelines and reverse-ETL workflows that sync enriched customer traits

Design and maintain intuitive, executive-ready Tableau dashboards

Implement automated testing, anomaly detection, and schema validation across customer data pipelines

What you’ll bring

All qualifications

Core experience

Experience: 2–4+ years of hands-on experience in a hybrid data role such as Analytics Engineer, Customer Data Engineer, BI Developer, or Marketing Data Engineer.
Python & Pipeline Engineering: Demonstrated experience using Python for data transformation, API integrations, and workflow automation.
Communication & Collaboration: Proven track record of presenting complex data insights to non-technical stakeholders and driving cross-functional alignment.

Preferred experience

Prior experience in fintech, commercial banking, or digital banking platforms is preferred.
Experience scheduling and orchestrating DAGs using tools like Apache Airflow or Google Cloud Composer is preferred.
Familiarity with reverse-ETL workflows or customer data activation is a plus.

Qualification wording

Experience: 2–4+ years of hands-on experience in a hybrid data role such as Analytics Engineer, Customer Data Engineer, BI Developer, or Marketing Data Engineer. Prior experience in fintech, commercial banking, or digital banking platforms is preferred.

Python & Pipeline Engineering: Demonstrated experience using Python for data transformation, API integrations, and workflow automation. Experience scheduling and orchestrating DAGs using tools like Apache Airflow or Google Cloud Composer is preferred.

Communication & Collaboration: Proven track record of presenting complex data insights to non-technical stakeholders and driving cross-functional alignment.

Customer Analytics Acumen: Strong conceptual understanding of customer lifecycle metrics: Customer Acquisition Cost (CAC), Lifetime Value (LTV), cohort retention, and churn. Familiarity with reverse-ETL workflows or customer data activation is a plus.

Education & alternatives

- Communication & Collaboration: Proven track record of presenting complex data insights to non-technical stakeholders and driving cross-functional alignment. - Education: Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Mathematics, Engineering, or equivalent practical experience. Employment type

Apply at Grasshopper Bank

Posting record

Report an error

Actively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Oct 30, 20265 days ago
Recorded sightings: 6
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$122K–$190K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 321 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 5

Early. Similar jobs usually come down between day 6 and day 47.

From 1,405 similar postings that came down. Coming down is not always a hire.

Grasshopper Bank is hiring

1 open job

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
SQL
BigQuery
dbt
Tableau
Airflow
GCP

Where the posting names them

In this high-impact role, you will move beyond standard descriptive reporting to build the foundational data models, pipelines, and semantic layers that explain who our clients are, how they interact with our digital banking platforms, and what drives long-term customer lifetime value (LTV). You will be responsible for modeling complex customer event streams, digital touchpoints, and transaction histories into analytics-ready models in Google BigQuery, powering executive Tableau reporting and directly activating customer data downstream into marketing and engagement engines (such as Braze). If you love writing clean, performant SQL/Python transformation logic, unraveling complex customer journeys, and partnering with Product and Marketing leaders to unlock top-line revenue, this role is for you. - Customer 360 & C-MDM Modeling: Design, build, and optimize scalable dimensional models (Kimball methodology) and One Big Table (OBT) datasets in Google BigQuery that unify core banking records, digital session logs, and touchpoints into a trusted, bank-wide customer master (C-MDM) and single source of truth.

- Semantic Layer & Master Data Development: Formalize bank-wide customer entities, core segmentations, and Critical Data Elements (CDEs)—such as Active User definitions (DAU/MAU), primary operating account status, feature adoption, and dormancy thresholds—to align business and technical definitions under a unified C-MDM framework. - Scalable ELT Pipelines: Design, deploy, and maintain resilient ELT/ETL pipelines (using SQL, Python, and orchestration tools) to ingest, clean, and enrich raw customer interaction data, API payloads, and core snapshot tables. - Downstream Marketing & Activation Sync: Engineer reliable pipelines and reverse-ETL workflows that sync enriched customer traits, behavioral events, and engagement tiers directly into marketing automation platforms (e.g., Braze) and CRM systems to power automated lifecycle campaigns.

- Dimensional & Event Data Modeling: Strong experience with dimensional modeling, star schemas, and structuring behavioral/event-based data models (hands-on experience with dbt for modeling transformations and lineage is a strong plus). - Python & Pipeline Engineering: Demonstrated experience using Python for data transformation, API integrations, and workflow automation. Experience scheduling and orchestrating DAGs using tools like Apache Airflow or Google Cloud Composer is preferred. - Tableau Expertise: Deep proficiency in Tableau Desktop and Tableau Server/Cloud, including Level of Detail (LOD) expressions, parameters, and thoughtful dashboard UX/UI design principles.

- Experience: 2–4+ years of hands-on experience in a hybrid data role such as Analytics Engineer, Customer Data Engineer, BI Developer, or Marketing Data Engineer. Prior experience in fintech, commercial banking, or digital banking platforms is preferred. - Advanced SQL & BigQuery: Expert-level SQL skills, including advanced analytical window functions, complex CTEs, array/struct unnesting, and performance tuning within cloud data warehouses (Google BigQuery strongly preferred). - Dimensional & Event Data Modeling: Strong experience with dimensional modeling, star schemas, and structuring behavioral/event-based data models (hands-on experience with dbt for modeling transformations and lineage is a strong plus).

As a Customer Analytics Engineer, you will operate at the intersection of modern data engineering, customer behavior analytics, and growth enablement. Reporting to the Director of BI & Analytics, you will serve as the technical engine and strategic architect behind our bank-wide Customer 360 and customer lifecycle analytics initiatives. In this high-impact role, you will move beyond standard descriptive reporting to build the foundational data models, pipelines, and semantic layers that explain who our clients are, how they interact with our digital banking platforms, and what drives long-term customer lifetime value (LTV). You will be responsible for modeling complex customer event streams, digital touchpoints, and transaction histories into analytics-ready models in Google BigQuery, powering executive Tableau reporting and directly activating customer data downstream into marketing and engagement engines (such as Braze). If you love writing clean, performant SQL/Python transformation logic, unraveling complex customer journeys, and partnering with Product and Marketing leaders to unlock top-line revenue, this role is for you.

If you love writing clean, performant SQL/Python transformation logic, unraveling complex customer journeys, and partnering with Product and Marketing leaders to unlock top-line revenue, this role is for you. - Customer 360 & C-MDM Modeling: Design, build, and optimize scalable dimensional models (Kimball methodology) and One Big Table (OBT) datasets in Google BigQuery that unify core banking records, digital session logs, and touchpoints into a trusted, bank-wide customer master (C-MDM) and single source of truth. - Identity Resolution, Governance & Attribution: Establish robust customer identity mapping and multi-touch marketing attribution models across diverse platforms, while championing master data management (C-MDM) principles, data quality, and governance across all customer data pipelines.

- Storytelling with Data: Translate complex, multi-layered data into clear, actionable narratives that answer the "so what?" for business line leaders and product managers. - Performance Optimization: Continuously monitor and tune BigQuery queries and Tableau extracts to ensure rapid workbook performance and cost-efficient query processing. - Strategic Growth Partner: Collaborate closely with Marketing, Product, and Business Line leaders to translate business hypotheses into quantitative frameworks and tracking requirements.

- Advanced SQL & BigQuery: Expert-level SQL skills, including advanced analytical window functions, complex CTEs, array/struct unnesting, and performance tuning within cloud data warehouses (Google BigQuery strongly preferred). - Dimensional & Event Data Modeling: Strong experience with dimensional modeling, star schemas, and structuring behavioral/event-based data models (hands-on experience with dbt for modeling transformations and lineage is a strong plus). - Python & Pipeline Engineering: Demonstrated experience using Python for data transformation, API integrations, and workflow automation. Experience scheduling and orchestrating DAGs using tools like Apache Airflow or Google Cloud Composer is preferred.

- Data Quality & Observability: Implement automated testing, anomaly detection, and schema validation across customer data pipelines to guarantee platform accuracy, uptime, and alerting. - Customer Journey & Cohort Dashboards: Design and maintain intuitive, executive-ready Tableau dashboards focusing on customer acquisition funnels, retention curves, product penetration, churn indicators, and deposit velocity. - Storytelling with Data: Translate complex, multi-layered data into clear, actionable narratives that answer the "so what?" for business line leaders and product managers.

- Python & Pipeline Engineering: Demonstrated experience using Python for data transformation, API integrations, and workflow automation. Experience scheduling and orchestrating DAGs using tools like Apache Airflow or Google Cloud Composer is preferred. - Tableau Expertise: Deep proficiency in Tableau Desktop and Tableau Server/Cloud, including Level of Detail (LOD) expressions, parameters, and thoughtful dashboard UX/UI design principles. - Customer Analytics Acumen: Strong conceptual understanding of customer lifecycle metrics: Customer Acquisition Cost (CAC), Lifetime Value (LTV), cohort retention, and churn. Familiarity with reverse-ETL workflows or customer data activation is a plus.

Upload resume Add LinkedIn

Your next step

Complete your application on grasshopper.pinpointhq.com. The employer’s form will show what is required.

Applying through Pinpoint

Only .pdf and .docx are accepted. Older .doc files are turned away.

Already applied? Track this application

About the role

We file this job under Analytics Engineer, by the work the posting describes.

Analytics engineers turn raw warehouse data into clean, tested and documented tables that analysts and dashboards can rely on, often with dbt.

Expect strong SQL, data modeling, Git, and a warehouse such as Snowflake, BigQuery or Redshift.

A data engineer gets data into the warehouse; an analytics engineer shapes it once it is there and works closer to the analysts who use it.

US government figures for database architects

Median yearly pay, May 2025: $139,500
Expected change, 2025 to 2035: 9.4% more jobsagainst 3.5% for all jobs

The government has no code for analytics engineers, so these figures are for the closest group, database architects, the same group we use for data engineers.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

Grasshopper Bank is a client-first, digital bank built for the business and innovation economy, with an obsession for providing leading-edge technology, solutions-driven products and superior service through a combination of passionate people and digital resources.

We are a highly experienced team who pride ourselves on diversity of thought and perspective. Nationally chartered, our portfolio of products and services range from VC, PE & Portfolio Companies to SBA Lending, as well as direct SMB and Embedded Banking.

Our entrepreneurial drive allows us to support the growth and success of a wide range of clients at every stage of their business through inclusive partnership. We seek out team members who will enable both our organization and our people to grow and thrive through collaboration and acting with integrity and respect.

Our focus on cross-functional teamwork provides a culture where ideas are valued, accountability is encouraged, and successes are celebrated. We welcome all those searching for the opportunity to contribute to banking innovation that influences and supports the emerging digital world of Financial Services.

Our digital first approach enables our teams the flexibility to work remotely. We have offices in NYC and Boston.

What you'll do:

As a Customer Analytics Engineer, you will operate at the intersection of modern data engineering, customer behavior analytics, and growth enablement. Reporting to the Director of BI & Analytics, you will serve as the technical engine and strategic architect behind our bank-wide Customer 360 and customer lifecycle analytics initiatives.

In this high-impact role, you will move beyond standard descriptive reporting to build the foundational data models, pipelines, and semantic layers that explain who our clients are, how they interact with our digital banking platforms, and what drives long-term customer lifetime value (LTV). You will be responsible for modeling complex customer event streams, digital touchpoints, and transaction histories into analytics-ready models in Google BigQuery, powering executive Tableau reporting and directly activating customer data downstream into marketing and engagement engines (such as Braze).

If you love writing clean, performant SQL/Python transformation logic, unraveling complex customer journeys, and partnering with Product and Marketing leaders to unlock top-line revenue, this role is for you.

Customer 360 & C-MDM Modeling: Design, build, and optimize scalable dimensional models (Kimball methodology) and One Big Table (OBT) datasets in Google BigQuery that unify core banking records, digital session logs, and touchpoints into a trusted, bank-wide customer master (C-MDM) and single source of truth.
Identity Resolution, Governance & Attribution: Establish robust customer identity mapping and multi-touch marketing attribution models across diverse platforms, while championing master data management (C-MDM) principles, data quality, and governance across all customer data pipelines.
Semantic Layer & Master Data Development: Formalize bank-wide customer entities, core segmentations, and Critical Data Elements (CDEs)—such as Active User definitions (DAU/MAU), primary operating account status, feature adoption, and dormancy thresholds—to align business and technical definitions under a unified C-MDM framework.
Scalable ELT Pipelines: Design, deploy, and maintain resilient ELT/ETL pipelines (using SQL, Python, and orchestration tools) to ingest, clean, and enrich raw customer interaction data, API payloads, and core snapshot tables.
Downstream Marketing & Activation Sync: Engineer reliable pipelines and reverse-ETL workflows that sync enriched customer traits, behavioral events, and engagement tiers directly into marketing automation platforms (e.g., Braze) and CRM systems to power automated lifecycle campaigns.
Data Quality & Observability: Implement automated testing, anomaly detection, and schema validation across customer data pipelines to guarantee platform accuracy, uptime, and alerting.
Customer Journey & Cohort Dashboards: Design and maintain intuitive, executive-ready Tableau dashboards focusing on customer acquisition funnels, retention curves, product penetration, churn indicators, and deposit velocity.
Storytelling with Data: Translate complex, multi-layered data into clear, actionable narratives that answer the "so what?" for business line leaders and product managers.
Performance Optimization: Continuously monitor and tune BigQuery queries and Tableau extracts to ensure rapid workbook performance and cost-efficient query processing.
Strategic Growth Partner: Collaborate closely with Marketing, Product, and Business Line leaders to translate business hypotheses into quantitative frameworks and tracking requirements.
Data Stewardship & Literacy: Champion data governance best practices, document customer data lineages and KPI definitions in the Data Steward Playbook, and coach end-users on self-service data discovery.

What you need:

Experience: 2–4+ years of hands-on experience in a hybrid data role such as Analytics Engineer, Customer Data Engineer, BI Developer, or Marketing Data Engineer. Prior experience in fintech, commercial banking, or digital banking platforms is preferred.
Advanced SQL & BigQuery: Expert-level SQL skills, including advanced analytical window functions, complex CTEs, array/struct unnesting, and performance tuning within cloud data warehouses (Google BigQuery strongly preferred).
Dimensional & Event Data Modeling: Strong experience with dimensional modeling, star schemas, and structuring behavioral/event-based data models (hands-on experience with dbt for modeling transformations and lineage is a strong plus).
Python & Pipeline Engineering: Demonstrated experience using Python for data transformation, API integrations, and workflow automation. Experience scheduling and orchestrating DAGs using tools like Apache Airflow or Google Cloud Composer is preferred.
Tableau Expertise: Deep proficiency in Tableau Desktop and Tableau Server/Cloud, including Level of Detail (LOD) expressions, parameters, and thoughtful dashboard UX/UI design principles.
Customer Analytics Acumen: Strong conceptual understanding of customer lifecycle metrics: Customer Acquisition Cost (CAC), Lifetime Value (LTV), cohort retention, and churn. Familiarity with reverse-ETL workflows or customer data activation is a plus.
Communication & Collaboration: Proven track record of presenting complex data insights to non-technical stakeholders and driving cross-functional alignment.
Education: Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Mathematics, Engineering, or equivalent practical experience.

Employment type

Full Time

Source details