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Build the Future of Data Engineering Data engineers used to spend their days building pipelines. Today's data engineers build intelligent platforms, automate everything that can be automated, collaborate with AI to accelerate delivery, and still have enough curiosity left over to ask, "There has to be a smarter way to do this." We're looking for a Senior Data Engineer who thrives on solving complex problems, building scalable data products, and exploring how AI can transform the way software is developed. You'll join a team that believes engineering is about more than writing code. It's about innovation, continuous learning, craftsmanship, and creating solutions that deliver measurable value. In this role, you'll design scalable data platforms, build trusted data products, leverage modern cloud technologies, and embrace AI-assisted engineering practices to solve problems faster and more effectively. You'll work alongside talented engineers, data scientists, analysts, and business partners to power analytics, decision-making, and emerging AI capabilities across the enterprise. We believe the best engineers don't simply adopt new tools. They rethink how work gets done. If you're energized by difficult challenges, excited by rapidly evolving technology, and curious enough to question established approaches, we'd love to meet you. Build Data Products That Matter Design and build scalable data platforms, pipelines, and services that power analytics, business decisions, machine learning, and AI-enabled solutions. Transform complex, high-volume, and messy datasets into trusted, well-governed data products that teams depend on every day. Create reusable frameworks, APIs, and self-service capabilities that improve data accessibility and developer productivity. Design data models and architectures optimized for analytics, reporting, AI, and advanced data workloads. Continuously improve platform performance, scalability, reliability, observability, and security. Engineer Smarter with AI Leverage AI-assisted development tools to accelerate coding, testing, debugging, documentation, and solution design while maintaining high quality standards. Apply AI to eliminate repetitive work, automate engineering tasks, and improve delivery efficiency. Experiment with emerging AI engineering capabilities and identify opportunities to improve team productivity and software quality. Use engineering judgment to validate AI-generated outputs and ensure solutions remain secure, maintainable, and production-ready. Help define and evolve best practices for AI-augmented software development across the organization. Solve Complex Problems Tackle challenging data integration, data quality, performance, and reliability problems across enterprise-scale systems. Investigate production issues, identify root causes, and implement long-term solutions rather than temporary fixes. Design robust monitoring, alerting, lineage, and observability capabilities that increase trust in data. Improve processes and systems so that teams can focus more time on innovation and less time on operational overhead. Collaborate and Lead Partner with analysts, data scientists, architects, software engineers, and business stakeholders to deliver impactful solutions. Translate business objectives into scalable technical architectures and implementation strategies. Lead initiatives from design through deployment and operational support. Mentor engineers, share knowledge, conduct thoughtful code reviews, and help raise the engineering bar across the team. Contribute to innovation initiatives, proof-of-concepts, hackathons, and emerging technology experiments. What We're Looking For Technical Foundations 8+ years of experience in data engineering, software engineering, analytics engineering, or related technical disciplines. Proven experience designing and implementing scalable data pipelines and cloud-based data platforms. Strong expertise in SQL, data modeling, and relational and non-relational database technologies. Strong programming skills in Python and at least one additional language such as Scala or Java. Experience with modern cloud platforms such as AWS, Azure, or GCP. Hands-on experience with technologies such as Spark, Databricks, Snowflake, Kafka, Hadoop, or similar modern data platforms. Strong understanding of data quality, governance, security, observability, lineage, and operational excellence practices. AI-Augmented Engineering Mindset We're less interested in whether you've memorized every AI acronym and more interested in how you think. Successful candidates will typically demonstrate: Experience using AI coding assistants and AI-enabled engineering tools to improve productivity and software quality. Curiosity about emerging technologies and a willingness to experiment with new approaches. The ability to combine AI-generated outputs with sound engineering judgment. A mindset focused on continuous learning, innovation, and continuous improvement. A belief that great engineers don't compete with AI. They learn how to partner with it. Leadership and Collaboration Experience leading technical initiatives and influencing engineering decisions. Strong communication and collaboration skills. Ability to navigate ambiguity and transform ideas into practical solutions. Passion for mentoring teammates and helping others grow. A bias toward ownership, curiosity, and continuous improvement. Bonus Points! We'll be especially excited if you have experience with: Machine learning, GenAI, agentic AI, or advanced analytics platforms. Building data products that support AI and machine learning workloads. Platform engineering, Infrastructure as Code, DevOps, and CI/CD automation. Using AI to significantly improve engineering productivity, quality, or delivery outcomes. Cloud, Databricks, Snowflake, or related technical certifications. Open-source contributions, technical blogging, public speaking, or engineering community leadership. We believe modern engineering is about more than writing code . Special Factors Sponsorship Vanguard is not offering visa sponsorship for this position. About Vanguard At Vanguard, we don't just have a mission—we're on a mission. To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best. How We Work Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.