About Airwallex Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 200,000 businesses worldwide - including Brex, Rippling, Navan, Qantas, SHEIN and many more - with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale. Proudly founded in Melbourne, we have a team of over 2,200 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$8 billion and backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, Airwallex is leading the charge in building the global payments and financial platform of the future. If you're ready to do the most ambitious work of your career, join us. Attributes We Value We hire successful builders with founder-like energy who want real impact, accelerated learning, and true ownership. You bring strong role-related expertise and sharp thinking, and you're motivated by our mission and operating principles. You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor. You're humble and collaborative; turn zero-to-one ideas into real products, and you "get stuff done" end-to-end. You use AI to work smarter and solve problems faster. Here, you'll tackle complex, high-visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let's build what's next. About the team: The Knowledge Platform team is at the heart of our company's data and AI strategy. We are building the foundational infrastructure that empowers the entire company to leverage data, AI, and ML into business impact. We accomplish this by creating platforms that handle the entire data and AI/ML lifecycle, simplifying the interface while providing proper safety and governance. This includes managing our data infrastructure (Databricks, Spark, Kafka, etc.), the technology to serve that data to our users (RAG, MCP, etc.), and the platform to host and govern these AI/ML models. In 2026, our team's overarching mission is to evolve our full data ecosystem-encompassing both platform and models-into a fully AI agent-ready infrastructure; we will empower customers to engage directly with the data platform to extract actionable value through capabilities like analytics and natural language querying, while also upgrading the platform to deliver robust, real-time performance for instant, data-driven decision-making. What you'll do In this role, you will lead the architecture and construction of core infrastructure components, taking full ownership of technical decision-making and custom development from foundational storage to high-availability service layers. You will perform deep-dives into systems internals and performance tuning-focusing on state management, checkpointing, and exactly-once semantics-to ensure millisecond-level accuracy and reliability across 30+ global regions. A significant portion of your impact will involve developing the "Knowledge Platform" to power AI-driven products through scalable vector indexing, agentic workflows, and real-time data streaming. Beyond the initial build, you will own the full SDLC for high-performance distributed systems that process petabytes of data at a global scale. As a champion of engineering rigor, you will advocate for elite software practices and contribute to shared tooling, SDKs, and automation frameworks that enhance productivity across the entire organization. Finally, you will serve as a technical beacon and mentor for mid-level engineers, driving high-quality project delivery through meticulous design reviews and hands-on technical leadership. This is a hybrid role based in San Francisco. Who you are: - Experience: Have 5+ years of experience building and operating large-scale distributed systems or infrastructure platforms. - First Principles Thinking: Possess a deep understanding of computer science fundamentals, including distributed systems, memory management, and networking protocols. - The "Builder" Stack: Are proficient in Java, Kotlin, or Go. You should have hands-on experience (or the desire to deep-dive) into the internals of Kafka, Flink, Spark, Kubernetes, or OLAP engines. - Ownership Mindset: Have a proven track record of taking 0-1 ownership of complex technical challenges, from initial design to production stability. - Curiosity & Impact: Are intrinsically motivated to explore emerging tech in AI/ML infrastructure and real-time systems to create tangible business impact. Preferred Qualifications: - Hands-on design experience in crafting data processing patterns for a modern Lakehouse architecture. - Contribute to the design and development of standard framework modules, high-performance services, and client libraries for big data using tools like GCP, Databricks, BigQuery, DataProc, Kafka, Kubernetes, Spark, DataFlow, Google Cloud Storage, and Airflow. - Excellent written and verbal communication skills tailored for diverse audiences (leadership, users, company-wide). - Ability to rapidly evaluate various technologies and conduct proof of concepts to drive architecture design. - Experience thriving in a complex environment. Applicant Safety Policy: Fraud and Third-Party Recruiters To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @airwallex.com email address. Please apply only through careers.airwallex.com or our official LinkedIn page. Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary. Equal opportunity Airwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don't regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know. #BI-Hybrid

Working at Atlassian Atlassians can choose where they work - whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Our Product DE organization is looking for a Senior Data Engineer reporting to the Senior Data Engineering Manager. You will be responsible for designing premier data architecture and solutions that drive strategic decision-making. Beyond technical execution, you will collaborate with business stakeholders to translate operational needs into technical requirements and spearhead initiatives to improve overall system efficiency. In this role, you will: - Partner across engineering organizations to spearhead and execute high-impact, company-wide initiatives, - Mentor and upskill junior engineers, fostering a culture of technical excellence and continuous growth. - Collaborate with leadership, product engineers, program managers, and data scientists to deeply understand and prioritize business data requirements. - Leverage deep technical expertise to design and architect scalable, high-performance data solutions. - Develop, launch, and optimize highly efficient, reliable ELT/ETL pipelines capable of processing both large-scale and specialized datasets. - Intelligently design robust data models optimized for high-speed storage, accessibility, and downstream retrieval. - Implement and deploy rigorous data quality frameworks to guarantee data accuracy, freshness, and trust. - Maintain full technical ownership of the end-to-end data engineering lifecycle for your team's solutions. - Participate in the team's on-call rotation to ensure platform stability and support operational health. - Partner with software engineering teams to design and build next-generation data systems that enable rapid, self-service data consumption. - A BE in Computer Science or equivalent with 6+ years of professional experience as a Data Engineer or in a similar role - Experience building scalable data pipelines in Spark using Airflow scheduler/executor framework or similar scheduling tools. - Experience with Databricks and its APIs. - Experience with modern databases (Redshift, Dynamo DB, Mongo DB, Postgres or similar) and data lakes. - Proficient in one or more programming languages such as Python/Scala and rock-solid SQL skills. - Champion automated builds and deployments using CICD tools like Bitbucket, Git - Experience working with large-scale, high-performance data processing systems (batch and streaming) - Experience working for SAAS companies - Experience with Machine Learning - Committed code to open source projects - Experience building self-service tooling and platforms Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together. We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines. To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them. To learn more about our culture and hiring process, visit go.atlassian.com/crh .

Role Description Own specific set or group of data transfers in various capacities including: - Collection setup - Data transfer setup - Contributor/customer setup Oversee specific database management ensuring structure and dataflow adheres to department standards. Perform daily data loads ensuring recurring updates are logged and tracked. Utilize various data workflow management and analysis tools. Produce code that is efficient, repeatable, and adherent to best practices such as: - Naming conventions - Encapsulation Good level of intimacy within a specific data content area. Participate in process improvement and compliance to successfully and consistently deliver high quality services on time, and to specification, resulting in flexibility to react quickly to changes in priorities or circumstances to meet business needs. Write and review portions of detailed specifications for the development of data components of moderate complexity. Complete simple data engineering bug fixes and resolve technical issues as necessary. Work closely with other engineering team members to understand data and translate requirements. Operate in various development environments (Agile, Waterfall, etc.) while collaborating with key stakeholders. Participate in project reviews. Keep abreast of new technology developments. Perform other duties as needed. Qualifications - Bachelor’s degree (or foreign equivalent) in Computer Science, Computer Engineering, Information Systems, or a related field required. - 3 years of experience in job offered or related occupations required. - 3 years of experience working with simple data models. - 3 years of experience with development languages including but not limited to: Bash Scripting, .Net, SQL, Windows, Unix, HPCC. - 3 years of experience with industry best practices - Code coverage. - 3 years of experience with software development methodologies (e.g., Agile, Waterfall). - 3 years of experience with data manipulation languages. Requirements - Employee reports to LexisNexis Risk Data Management, LLC office in Alpharetta, GA, but may telecommute from any location within the U.S. - Experience can be concurrent. Benefits - Salary range for REQ# R115204: $110,000 to $119,400/year + standard company benefits. - National Base Pay Range: $71,600 - $119,400. Geographic differentials may apply in some locations to better reflect local market rates. - Country specific benefits available.

• Participate in the design, development, and maintenance of data pipelines using ETL/ELT processes to ingest, transform, and load data from various sources into the data warehouse. • Implement and maintain data quality checks and validation processes to ensure data accuracy and reliability. • Collaborate with data scientists and analysts to understand their data requirements and provide solutions to meet their analytical needs. • Contribute to the development and maintenance of data models, schemas, and dictionaries to ensure data consistency and usability. • Monitor data pipeline performance and troubleshoot data-related issues in a timely manner. • Assist in the evaluation and implementation of new data technologies and tools. • Develop and maintain documentation for data pipelines, processes, and data models. • Contribute to the development of data governance policies and procedures. • Participate in code reviews and knowledge sharing sessions with other data engineers. • Support the migration of existing data pipelines to more scalable and efficient solutions.