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Senior Data Engineer

SoFiCottonwood Heights, UtahOnsiteFULL_TIME$124,800–$156,000 per yearPosted Oct 23 · 1 day ago

How well this resume fits

Compared with a sample resume. Compare your own

Not sure The job lists no required items.

How close the work is

Moderate

3 of 6 named skills are shown in described work and 0 are only listed or mentioned (rule: met = 1, partial = ½; strong ≥ 75%, moderate ≥ 50%).

How strong your resume shows it

Strong

Of 4 supported items, 3 have strong evidence (a reported result or a confirmed answer) and 0 moderate (used in a described task).

What the job asks for

What the job asksRequired?Do you have it?From your resume
Skill Snowflake
Design, build, and maintain scalable data pipelines that consolidate fraud operational data from multiple source systems into governed Snowflake data marts
Not statedNot sure
The résumé doesn't mention Snowflake. That means unknown, not lacking. (Parsed from lower-quality page text; check it against the original posting.)
Nothing found
Skill Tableau
Build and maintain dbt models that structure data for easy consumption by analysts and Tableau reporting layers
Not statedYes
The résumé shows Tableau used with a reported result. (Parsed from lower-quality page text; check it against the original posting.)
Built a no-show analysis in Tableau that helped three clinics change reminder timing and lower no-shows by 11%.
Built and maintain 14 Tableau dashboards on SQL Server for on-time delivery, carrier cost and warehouse throughput, used by 60+ managers each week.
Loaded 30M trip records into PostgreSQL and used Python to study fares and tipping by time of day; published the findings with a Tableau Public dashboard.
Experience 4+ years of
4+ years of data engineering experience with strong hands-on expertise in SQL, Python, and cloud data warehouse platforms
Not statedPartly
The résumé shows 6.7 years in dated roles in total, but time spent on “strong hands-on expertise in SQL” specifically can't be measured from the dates. (Parsed from lower-quality page text; check it against the original posting.)
Data Analyst
Junior Data Analyst
Data Analytics Intern
Skill SQL
4+ years of data engineering experience with strong hands-on expertise in SQL, Python, and cloud data warehouse platforms
Not statedYes
The résumé shows SQL used with a reported result. (Parsed from lower-quality page text; check it against the original posting.)
Replaced a manual Excel carrier report with a scheduled Python and SQL job, saving the operations team about 8 hours a week.
Built and maintain 14 Tableau dashboards on SQL Server for on-time delivery, carrier cost and warehouse throughput, used by 60+ managers each week.
Pulled and cleaned patient visit data (about 2M rows a month) in SQL and Python for clinic scheduling reports.
Skill Python
4+ years of data engineering experience with strong hands-on expertise in SQL, Python, and cloud data warehouse platforms
Not statedYes
The résumé shows Python used with a reported result. (Parsed from lower-quality page text; check it against the original posting.)
Replaced a manual Excel carrier report with a scheduled Python and SQL job, saving the operations team about 8 hours a week.
Wrote a demand forecast in Python (pandas, statsmodels) for 120 lanes that cut forecast error from 22% to 14%.
Pulled and cleaned patient visit data (about 2M rows a month) in SQL and Python for clinic scheduling reports.
Skill dbt
Proven experience building and maintaining production-grade data pipelines using modern data stack tools (dbt, Airflow, or equivalent)
Not statedNot sure
The résumé doesn't mention dbt. That means unknown, not lacking. (Parsed from lower-quality page text; check it against the original posting.)
Nothing found
Skill Airflow
Proven experience building and maintaining production-grade data pipelines using modern data stack tools (dbt, Airflow, or equivalent)
Not statedNot sure
The résumé doesn't mention Airflow. That means unknown, not lacking. (Parsed from lower-quality page text; check it against the original posting.)
Nothing found
Strong data modeling skills — experience designing analytical data structures optimized for reporting and self-service consumptionNot statedNot sure
The rules can't check this against the résumé. (Parsed from lower-quality page text; check it against the original posting.)
Nothing found
Ability to work closely with analytics and business stakeholders to translate requirements into scalable data solutionsNot statedNot sure
The rules can't check this against the résumé. (Parsed from lower-quality page text; check it against the original posting.)
Nothing found
Strong documentation habits and commitment to data quality, testing, and pipeline reliabilityNot statedNot sure
The rules can't check this against the résumé. (Parsed from lower-quality page text; check it against the original posting.)
Nothing found
Experience in financial services, fraud, risk, or compliance data environmentsNot statedNot sure
The rules can't check this against the résumé. (Parsed from lower-quality page text; check it against the original posting.)
Nothing found
Familiarity with fraud operations data concepts — fraud tagging, loss attribution, dispute workflows, agent performance metrics, or alert queue managementNot statedNot sure
The rules can't check this against the résumé. (Parsed from lower-quality page text; check it against the original posting.)
Nothing found
How the match was worked out

Coverage: what the job asks, what the résumé shows, what is left

28% of the weighted requirements are covered by résumé evidence. required: met 2, partly 1, missing 7 · unknown: met 1, partly 0, missing 1

weight = 3 required / 2 preferred / 1 unknown, +1 emphasis (expert, strong, extensive…), +1 named in the job title, +½ per repeat (max +1); rating = 3 met, 2 partially met with the term in described work, 1.5 partially met (listed or mentioned), 0 unknown or mismatch; coverage = Σ weight×rating/3 ÷ Σ weight over rows that can be judged (work-authorization, location and clearance rows need your answer and are left out).

Missing from the résumé

  • required r6 Proven experience building and maintaining production-grade data pipelines using modern data stack tools (dbt, Airflow, or equivalent) (weight 5.0) missing: Manufacturing, ETL / ELT
  • required r7 Proven experience building and maintaining production-grade data pipelines using modern data stack tools (dbt, Airflow, or equivalent) (weight 4.5)
  • required r8 Strong data modeling skills — experience designing analytical data structures optimized for reporting and self-service consumption (weight 4.0) missing: Data modeling found: Reporting
  • required r10 Strong documentation habits and commitment to data quality, testing, and pipeline reliability (weight 4.0) missing: Documentation, ETL / ELT found: Data cleaning
  • required r9 Ability to work closely with analytics and business stakeholders to translate requirements into scalable data solutions (weight 3.0) missing: Stakeholder management found: Data analysis
  • required r11 Experience in financial services, fraud, risk, or compliance data environments (weight 3.0) missing: financial services, fraud, risk, Compliance
  • required r12 Familiarity with fraud operations data concepts — fraud tagging, loss attribution, dispute workflows, agent performance metrics, or alert queue management (weight 3.0) missing: loss attribution, dispute workflows, KPIs, alert queue management found: Operations
  • unknown r1 Design, build, and maintain scalable data pipelines that consolidate fraud operational data from multiple source systems into governed Snowflake data marts (weight 1.5) missing: Design, ETL / ELT found: Operations

Only partly shown

  • required r3 4+ years of data engineering experience with strong hands-on expertise in SQL, Python, and cloud data warehouse platforms (weight 4.0) missing: Cloud platforms found: Data warehousing

Needs your answer

Nothing depends on an answer.

Lines under a requirements heading the rules could not turn into an item

  • Required: Exposure to dbt Cloud, Airflow/MWAA, or similar orchestration platforms
  • Required: Strongly Preferred

What the role does (not weighted)

On the résumé: Operations, Data analysis, Reporting, Operations, Data warehousing, Tableau, SQL querying, Dashboards
Not on the résumé: Fraud Prevention Center, Excellence, Process improvement, Senior Data Engineer, Fraud, Fraud Operations Analytics, ETL / ELT, ETL / ELT, Design, Snowflake, Fraud Data Engineering, TDM, KPIs, Data modeling, dbt, Manufacturing, Leadership, Standards, Documentation, Compliance, Stakeholder management, Fraud Strategy, Fraud Platform, Risk Infrastructure, Data Engineering

What the résumé says (as the rules read it)

Skills: Python (supported by outcome), SQL (supported by outcome), Tableau (supported by outcome), Excel (supported by outcome), pandas (supported by outcome), PostgreSQL (used in task), A/B testing (listed), Git (listed), Matplotlib (listed), NumPy (listed), Power BI (listed)
Roles: Data Analyst at Harborline Logistics (Jun 2022 – Present); Junior Data Analyst at Cedar Ridge Health Network (Jul 2020 – May 2022); Data Analytics Intern at Lakeshore State University, Office of Institutional Research (May 2019 – Aug 2019)
Years in dated roles: 6.7
Education: B.S. in Statistics (completed)
Certifications: Tableau Desktop Specialist, Google Data Analytics Professional Certificate
Quantified achievements: 4

On the résumé but not asked by this job: Healthcare, Dashboards, Supply chain, Forecasting, Active Directory, Finance, Research, Spreadsheets, Statistics

Résumé terms the rules don't know: OH, Server, Network, Office, Access, NYC, Taxi Trip Analysis, Tableau Public, Harborline, Economics, Professional Certificate (matched by plain text only; tell us if one should be a skill)

Job description

Employee Applicant Privacy Notice

Who we are:

Shape a brighter financial future with us.

Together with our members, we’re changing the way people think about and interact with personal finance.

We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world.

The Role:

SoFi's Fraud Prevention Center of Excellence (CoE) is responsible for managing enterprise fraud threats across banking products and member-related activities. The Fraud Operations Analytics & Reporting team sits at the center of this mission — driving intelligence through data to improve operational efficiency, agent accountability, and fraud detection efficacy across all fraud product lines.

We are looking for a Senior Data Engineer to build and scale the data infrastructure that powers the Fraud CoE. This role is the foundational engineering resource for the Fraud Operations Analytics team, responsible for consolidating data pipelines into governed, source-of-truth data marts and enabling the analytical work that drives operational decisions at the highest levels of the organization.

Key Responsibilities

Data Infrastructure & Pipeline Development

- Design, build, and maintain scalable data pipelines that consolidate fraud operational data from multiple source systems into governed Snowflake data marts
- Partner with Fraud Data Engineering and the TDM platform team on the foundational Fraud CoE data buildout, including summarized metric tables that standardize reporting across all fraud product lines

Data Modeling & Warehouse Optimization

- Model analytical data within Snowflake to support fraud operations reporting, agent performance scorecards, and fraud tagging quality monitoring
- Build and maintain dbt models that structure data for easy consumption by analysts and Tableau reporting layers
- Ensure query performance, data freshness, and reliability across all fraud data mart tables powering production dashboards and leadership reports

Analytics Enablement

- Partner closely with fraud analysts to understand data requirements and translate them into robust, reusable data structures
- Develop and maintain summarized metric tables that lock reporting logic centrally, ensuring consistency across reporting

Governance & Standards

- Establish and enforce data engineering standards, documentation practices, and pipeline design patterns for the Fraud CoE
- Ensure all data assets are properly documented, tested, and governed to support audit and compliance requirements
- Serve as the data engineering subject matter expert in cross-functional discussions with Fraud Strategy, Fraud Platform, Risk Infrastructure, and Data Engineering partners

What We're Looking For

Required

- 4+ years of data engineering experience with strong hands-on expertise in SQL, Python, and cloud data warehouse platforms
- Proven experience building and maintaining production-grade data pipelines using modern data stack tools (dbt, Airflow, or equivalent)
- Strong data modeling skills — experience designing analytical data structures optimized for reporting and self-service consumption
- Ability to work closely with analytics and business stakeholders to translate requirements into scalable data solutions
- Strong documentation habits and commitment to data quality, testing, and pipeline reliability
- Exposure to dbt Cloud, Airflow/MWAA, or similar orchestration platforms

Strongly Preferred

- Experience in financial services, fraud, risk, or compliance data environments
- Familiarity with fraud operations data concepts — fraud tagging, loss attribution, dispute workflows, agent performance metrics, or alert queue management

Compensation and Benefits

The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.

To view all of our comprehensive and competitive benefits, visit our Benefits at SoFi page!

SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.

The Company hires the best qualified candidate for the job, without regard to protected characteristics.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

New York applicants: Notice of Employee Rights

SoFi is committed to an inclusive culture. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email [email protected].

We are unable to accommodate remote work from Hawaii, Alaska or Puerto Rico at this time.

Internal Employees

If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.