Data Engineer
TypeFull-time job
LocationUnited States
Posted2 hours ago
About CYBERA
Scamsdrained$450 billionfrom consumers last year, up 19% in two years, and the number keeps growing as scammer armies use AI at scale. Every bank, insurer, fintech, and crypto exchange is increasingly on the hook to cover losses as new regulations to protect consumers are enacted. Every fraud tool on the market today fails at this usecase, whenvictims legitimately authorize payments. To the bank, transactions look normal,controlsnever fire, and just like that the money is gone.
CYBERA goes upstream to disrupt thescameconomy. Our agenticscamdefense platform engages scammers at scale,turnthem into informants, andcapturethe mule accounts they plan to use before anyone loses money. Banks, insurers, and payment companies use CYBERA to know their bad accounts before money moves in and freeze payments to bad accounts before money heads out. When scammers are successful, CYBERA's agentic response engine traces, freezes, and helps recover funds, dramatically improving consumer outcomes. Leading banks, insurers, and crypto exchanges use CYBERA to cut fraud losses, speed up victim recovery, and turn everyscamattempt into intelligence that protects the next customer.
Every Cyberian is a disruptor, using AI for good to stop financial crime at scale. If taking on this$450 billionproblem sounds like your kind of work,we'dlike to meet you. Learn more at.
Our Values
At CYBERA, how we work matters as much as what we achieve. We Deliver the Outcome by staying curious, thinking things through, and focusing on real customer value. We Do It as a Team through collaboration, empathy, and respectful candor. And we Adapt and Own It, adapting quickly and staying focused when priorities change, as they often do in a growing company.
About the role
As a Data Engineer, you’ll build and own the data foundation behind CYBERA’s mule intelligence and scam prevention products. You will turn high-volume, messy operational data into fast, reliable, and trusted datasets that our analysts, products, and customers can use with confidence. Working closely with engineering and data analytics, you’ll own the data warehouse and lake, strengthen data quality and performance, and create scalable standards for modelling, lineage, and metric definitions. This is a hands-on role on a small team where you will have significant ownership and the agency to solve problems end to end.
What you'll do
Own our data warehouse and data lake end to end, from ingestion and storage design through modelling and serving
Build and operate the pipelines that move data from the CYBERA platform and the systems behind our mule intelligence into the warehouse
Transform messy operational data into clean, documented datasets that analysts, products, and customer-facing systems can rely on
Define core business rules and metrics centrally so teams get consistent answers from the same data
Improve query and platform performance by reviewing execution plans, tuning indexes, and deciding what should be materialized as data volumes grow
Implement data quality checks, monitoring, and alerting to identify issues before they affect the business or our customers
Maintain lineage and versioning for data produced by LLM pipelines so changes do not silently alter reported results
Partner with data analysts on metric definitions, investigate upstream issues, and support reliable dashboards and reporting
Collaborate with engineers on schema changes, migrations, backfills, and reviews of production data code
Qualifications
4 to 6 years of hands-on data engineering experience, including building or substantially evolving a data warehouse or data lake
Expert SQL skills and a strong track record of diagnosing and improving slow or complex queries
Hands-on experience with PostgreSQL, SQL Server, or both
Deep practical experience with performance tuning, execution plans, indexing, and resolving production slowdowns
Strong data-modelling fundamentals, including experience designing reliable incremental loads
Working knowledge of orchestration, transformation, monitoring, and data-testing practices
Experience applying Git, code review, and continuous integration practices to data code
A high-ownership mindset and the ability to identify, investigate, and resolve problems in a fast-moving environment
It’s a plus if you have
Administrative experience with Metabase or another business intelligence platform
Experience working with LLM or machine-learning data pipelines
Experience with fraud, fintech, cybersecurity, or other adversarial data
Originally posted on Himalayas
Scamsdrained$450 billionfrom consumers last year, up 19% in two years, and the number keeps growing as scammer armies use AI at scale. Every bank, insurer, fintech, and crypto exchange is increasingly on the hook to cover losses as new regulations to protect consumers are enacted. Every fraud tool on the market today fails at this usecase, whenvictims legitimately authorize payments. To the bank, transactions look normal,controlsnever fire, and just like that the money is gone.
CYBERA goes upstream to disrupt thescameconomy. Our agenticscamdefense platform engages scammers at scale,turnthem into informants, andcapturethe mule accounts they plan to use before anyone loses money. Banks, insurers, and payment companies use CYBERA to know their bad accounts before money moves in and freeze payments to bad accounts before money heads out. When scammers are successful, CYBERA's agentic response engine traces, freezes, and helps recover funds, dramatically improving consumer outcomes. Leading banks, insurers, and crypto exchanges use CYBERA to cut fraud losses, speed up victim recovery, and turn everyscamattempt into intelligence that protects the next customer.
Every Cyberian is a disruptor, using AI for good to stop financial crime at scale. If taking on this$450 billionproblem sounds like your kind of work,we'dlike to meet you. Learn more at.
Our Values
At CYBERA, how we work matters as much as what we achieve. We Deliver the Outcome by staying curious, thinking things through, and focusing on real customer value. We Do It as a Team through collaboration, empathy, and respectful candor. And we Adapt and Own It, adapting quickly and staying focused when priorities change, as they often do in a growing company.
About the role
As a Data Engineer, you’ll build and own the data foundation behind CYBERA’s mule intelligence and scam prevention products. You will turn high-volume, messy operational data into fast, reliable, and trusted datasets that our analysts, products, and customers can use with confidence. Working closely with engineering and data analytics, you’ll own the data warehouse and lake, strengthen data quality and performance, and create scalable standards for modelling, lineage, and metric definitions. This is a hands-on role on a small team where you will have significant ownership and the agency to solve problems end to end.
What you'll do
Own our data warehouse and data lake end to end, from ingestion and storage design through modelling and serving
Build and operate the pipelines that move data from the CYBERA platform and the systems behind our mule intelligence into the warehouse
Transform messy operational data into clean, documented datasets that analysts, products, and customer-facing systems can rely on
Define core business rules and metrics centrally so teams get consistent answers from the same data
Improve query and platform performance by reviewing execution plans, tuning indexes, and deciding what should be materialized as data volumes grow
Implement data quality checks, monitoring, and alerting to identify issues before they affect the business or our customers
Maintain lineage and versioning for data produced by LLM pipelines so changes do not silently alter reported results
Partner with data analysts on metric definitions, investigate upstream issues, and support reliable dashboards and reporting
Collaborate with engineers on schema changes, migrations, backfills, and reviews of production data code
Qualifications
4 to 6 years of hands-on data engineering experience, including building or substantially evolving a data warehouse or data lake
Expert SQL skills and a strong track record of diagnosing and improving slow or complex queries
Hands-on experience with PostgreSQL, SQL Server, or both
Deep practical experience with performance tuning, execution plans, indexing, and resolving production slowdowns
Strong data-modelling fundamentals, including experience designing reliable incremental loads
Working knowledge of orchestration, transformation, monitoring, and data-testing practices
Experience applying Git, code review, and continuous integration practices to data code
A high-ownership mindset and the ability to identify, investigate, and resolve problems in a fast-moving environment
It’s a plus if you have
Administrative experience with Metabase or another business intelligence platform
Experience working with LLM or machine-learning data pipelines
Experience with fraud, fintech, cybersecurity, or other adversarial data
Originally posted on Himalayas
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