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Analytics Engineer II

Pay
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Work setting
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Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Own data products end to end, from requirements through production operation

  • Design metric and check definitions that test whether financial data is correct and complete

  • Build pipelines and dimensional models, with documentation and data dictionaries

  • Turn discrepancy investigations into durable checks rather than one-off fixes

  • Build alerting and observability so issues reach a named owner

From the employer’s posting
Own data products end to end — from requirements through modeling, implementation, validation, and production operation
Design and maintain the metric and check definitions that determine whether financial data is correct and complete
Build and maintain the pipelines and transformations behind those metrics, including dimensional modeling, ETL design, and the documentation and data dictionaries
turn each finding into a durable check rather than a one-off fix
Build alerting and observability so that issues reach a named owner with enough context to act

What you’ll bring

All qualifications

Preferred experience

  • Familiarity with machine learning concepts, and a clear point of view on where predictive methods do and do not belong in a data quality context
  • Experience with financial data, reconciliation, ledgers, or regulated reporting
  • Experience building data-facing web applications or internal tools
  • Experience introducing data quality practices to teams that did not previously have them
Qualification wording
Familiarity with machine learning concepts, and a clear point of view on where predictive methods do and do not belong in a data quality context
Experience with financial data, reconciliation, ledgers, or regulated reporting
Experience building data-facing web applications or internal tools
Experience introducing data quality practices to teams that did not previously have them

Benefits in the posting

Full benefits wording
  • Rice Allowance
  • Transportation Allowance
  • Paid Vacation
  • Medical, Dental & Vision
  • Accident and Life Insurance
  • Employee Stock Purchase Plan (ESPP)
  • Mental Health & Family Forming Benefits
  • Remitly is an E-Verify Employer

From the employer’s posting.

Apply at Remitly

Posting record

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Status in our records
Live
First seen by us
Oct 2, 202633 days ago
Recorded sightings
73
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

Time since we first saw it

Day 33

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.

Remitly is hiring

10 open jobs

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.

  • Java
  • Python
  • Scala
  • SQL
  • AWS
  • Redshift
  • Snowflake
  • Spark
  • Trino
  • Airflow
Where the posting names them
- 5+ years as an analytics engineer or equivalent - Expert in SQL, git, and a programming language (Python, Scala, Java, or similar) - Experience in dimensional data modeling, ETL design, implementation and maintenance
- Knowledge of logging, error handling, and familiarity with CI/CD concepts - Familiarity with AWS products and services - Experience with analytical model development and deployment processes
- Experience in dimensional data modeling, ETL design, implementation and maintenance - Experience with distributed query engines and cloud data platforms (Trino, Spark, Snowflake/Redshift or equivalent) and with workflow orchestration (Azkaban, Airflow or equivalent) - Knowledge of logging, error handling, and familiarity with CI/CD concepts

Your next step

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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 ↗

Job Description:

Remitly is on a mission to transform the lives of immigrants and their families by providing the most trusted financial products and services on the planet. Since 2011, we have been tirelessly delivering on our promises to immigrants sending their hard earned money home. Today, we are proud to have served millions of customers globally with Remitly and our newly launched Passbook app to provide immigrants access to banking. We strive daily to meet our promise to our customers by building peace of mind into everything we do.

About the Role:


The Reconciliation Insights and Analytics team builds important financial products and tools that ensure the accuracy and completeness of our financial data. We focus on analyzing and detecting data discrepancies, improving customer experience, and delivering business insight. Our data supports data consumers like Accounting, Treasury, Finance, and Customer Success.  You will join a team that owns the platform end to end, data modeling, pipelines, detection, alerting, and the agent workflows that triage and resolve what the platform finds. 


You will report to the Manager, Data Analytics.


Job Responsibilities:

  • Own data products end to end — from requirements through modeling, implementation, validation, and production operation
  • Design and maintain the metric and check definitions that determine whether financial data is correct and complete, and onboard new business domains onto the platform
  • Build and maintain the pipelines and transformations behind those metrics, including dimensional modeling, ETL design, and the documentation and data dictionaries that make them usable by others
  • Contribute to the platform’s application and service layer — the interfaces, services and deployments through which partners consume the platform’s output
  • Investigate discrepancies and anomalies to root cause, and turn each finding into a durable check rather than a one-off fix
  • Use AI agents as a primary working method — to model and reverse-engineer data semantics, to implement and ship production code, and to design the agent workflows the platform itself runs on
  • Validate what AI produces: define the acceptance criteria, evaluation, and review that make agent-generated work safe to ship, and apply domain judgment where correctness cannot be automated
  • Build alerting and observability so that issues reach a named owner with enough context to act, and so the platform’s own health is visible
  • Work with partner teams to understand requirements and ensure data solutions align with Remitly’s goals
  • Communicate complex data findings and technical information to non-technical partners in Accounting, Treasury, Finance and Compliance, both verbally and through data visualizations
  • Identify ways to improve the reliability, efficiency and quality of the platform, so coverage grows faster than the effort required to operate it

Job Qualifications:

  • 5+ years as an analytics engineer or equivalent
  • Expert in SQL, git, and a programming language (Python, Scala, Java, or similar)
  • Experience in dimensional data modeling, ETL design, implementation and maintenance
  • Experience with distributed query engines and cloud data platforms (Trino, Spark, Snowflake/Redshift or equivalent) and with workflow orchestration (Azkaban, Airflow or equivalent)
  • Knowledge of logging, error handling, and familiarity with CI/CD concepts
  • Familiarity with AWS products and services
  • Experience with analytical model development and deployment processes
  • Strong communication skills and ability to collaborate effectively with stakeholders
  • You live our cultural values

AI Proficiency:

This role uses AI as a core engineering method, not as an occasional assist. We expect proficiency across three dimensions, and we will ask you to show your work in each:

  • Modeling — you use AI to reason about unfamiliar data: reverse-engineering semantics from production tables, proposing metric and check definitions, and stress-testing a data model before you build it.
  • Implementation — you ship production code with coding agents as your default method, and can point to work that reached production this way.
  • Architecting — you design agentic systems themselves: multi-step workflows, tool and skill design, context and prompt structure, and the evaluation harness that determines whether a workflow is good enough to run unattended.

Most importantly, you can describe how you validate what an agent produces — what you check, what you automate, and where you decide a human must stay in the loop. In a data quality function, being fast is worth nothing if the output cannot be trusted. We care more about this judgment than about any particular tool, model or framework.


Preferred Qualifications:

  • A software engineering background. You have built and operated production services, not only pipelines and analyses — application code, testing, code review, deployment, and responsibility for something while it is running. This matters more than usual here: the platform is a product with users, and the bar for reviewing agent-written code is set by people who have written and maintained systems themselves.
  • Familiarity with machine learning concepts, and a clear point of view on where predictive methods do and do not belong in a data quality context
  • Experience with financial data, reconciliation, ledgers, or regulated reporting
  • Experience building data-facing web applications or internal tools
  • Experience introducing data quality practices to teams that did not previously have them

Our Benefits:

  • Rice Allowance
  • Transportation Allowance
  • Paid Vacation
  • Medical, Dental & Vision
  • Accident and Life Insurance
  • Employee Stock Purchase Plan (ESPP)
  • Mental Health & Family Forming Benefits

Our Connected Work Culture: Driving Innovation, Together

At Remitly, we believe that true innovation sparks when we come together. Our Connected Work Culture fosters dynamic in-person collaboration, where ideas ignite and challenging problems find solutions faster. For corporate team members, we have an in-office expectation of at least 50% of the time monthly, typically achieved by coming in three days a week. This creates a consistent, meaningful overlap that supports team norms and business needs. Managers also have the flexibility to set higher expectations based on their team's specific needs. These intentional in-office moments are vital for deepening relationships, fueling creativity, and ensuring your impact is felt where it matters most.


Remitly is an E-Verify Employer

At Remitly, we are dedicated to ensuring that our workplace offers equal employment opportunities to all employees and candidates, in full compliance with applicable laws and regulations.


Remitly is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

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