Financial Data Engineer Remote information
See Provo, UT salary details
$42.1K - $53.5K
0% of jobs
$76.4K - $87.8K
4% of jobs
$87.8K - $99.3K
7% of jobs
$109.3K is the 25th percentile. Wages below this are outliers.
$99.3K - $110.7K
11% of jobs
$110.7K - $122.2K
12% of jobs
The median wage is $125.2K / yr.
$122.2K - $133.6K
45% of jobs
$133.6K - $145K
8% of jobs
$145K - $156.5K
5% of jobs
$156.5K - $167.9K
3% of jobs
How much do financial data engineer remote jobs pay per year?
As of Aug 13, 2026, the average yearly pay for financial data engineer remote in Provo, UT is $122,707.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,300.00 and $130,100.00 per year, depending on experience, location, and employer.
A Financial Data Engineer designs, builds, and maintains systems that process and analyze large sets of financial data. Working remotely, they collaborate with teams to develop data pipelines, integrate financial databases, and ensure the reliability of data used for financial analysis and reporting. They often use programming languages like Python or SQL, and work with big data tools to support data-driven decision-making for financial institutions or fintech companies. Their work is crucial to transforming raw financial data into actionable insights.
Remote Financial Data Engineers often work closely with data analysts, software developers, and business stakeholders across different time zones. One common challenge is ensuring effective communication and alignment on project requirements, especially when dealing with complex financial data pipelines and evolving business needs. Utilizing collaborative tools, maintaining clear documentation, and participating in regular virtual meetings can help bridge gaps and foster productive teamwork. Staying proactive about updates and being responsive to feedback are key to ensuring smooth collaboration in a remote environment.
To thrive as a Financial Data Engineer (Remote), you need strong programming skills (such as Python or SQL), experience with data modeling, and a background in finance or quantitative analysis, often supported by a relevant degree. Proficiency with big data platforms (like Hadoop or Spark), ETL tools, and cloud data services (such as AWS or Azure) is typically required, alongside certifications in data engineering or finance. Excellent problem-solving, communication, and time management skills help you collaborate effectively and independently in a distributed environment. These capabilities are crucial for building reliable financial data pipelines, ensuring data quality, and supporting timely, data-driven business decisions.
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