We represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read LessWe represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read LessWe represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read LessWe represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read LessWe represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read LessWe represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read LessWe represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read LessWe represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read LessWe represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read LessWe represent a rapidly growing data company in NYC that’s redefining how real-world assets are represented and traded on public blockchains. Their platform serves investors, issuers, and financial institutions by providing reliable analytics, market intelligence, and transparent data on tokenized assets across the globe.
They’re trusted by leading players in finance and blockchain for their accuracy, scale, and forward-thinking approach to digital asset infrastructure. It’s an exciting opportunity to join a team that’s helping shape the future of real-world asset tokenization and build technology that’s changing how the financial world connects.
ResponsibilitiesBuild and scale core data systems and APIs that serve product-level analytics
Collaborate with application engineers to ensure clean data flow between backend systems and end-user features
Develop and optimize data pipelines using PySpark and Databricks
Work closely with the lead data engineer on system architecture and data infrastructure design
Participate in system design discussions focused on scalability, performance, and maintainability
Contribute to the full software development lifecycle, from design through deployment
Support product and engineering teams by turning raw data into usable insights
&
Ideal Background4 to 5+ years of software engineering experience, preferably focused on large-scale data systems
Strong proficiency in Python and experience with PySpark
Experience with distributed frameworks such as Apache Spark, Beam, Flink, or Kafka Streams
Proven ability to design, build, and maintain production-grade data pipelines and APIs
Background in computer science, computer engineering, applied mathematics, or a related field (top 50 university or equivalent rigor preferred)
Experience working on data-driven products rather than internal BI or reporting systems
Strong communication skills and the ability to explain technical tradeoffs clearly
High attention to detail, ownership mindset, and a passion for building high-quality systems
Experience in fintech, blockchain, or other data-intensive environments
Hands-on experience with Databricks or real-time streaming data systems
Demonstrated curiosity and craftsmanship through side projects or open-source work
Read Less