Deep Labs is a rapidly growing artificial intelligence company that helps businesses reduce fraud and identity theft, optimize customer experience, and make better business decisions.
Our Mission: At Deep Labs, we believe Persona-Based Intelligence is the next generation of true context-aware computing. Our mission is to be the world leader in the delivery of persona-based decisioning through innovation and technology, enabling our clients to make faster and better decisions while improving customer experience.
As a Graph Data Engineer (Public Sector), reporting to the Director of Data Science, you'll speak up, solve problems, lead others, and be an owner in your role. If you enjoy the experience of turning the chaos of messy data into beautiful adaptable structures, live in the clouds both in terms of delivery and creative thinking, are opinionated on design, and prefer to take ownership of tasks – you are right for this role. If you understand that documentation is the heart of everything and are always the one to point out that more time should be spent on it, we want you!
Role & responsibilities
- Own our data pipeline, the entirety of the data lifecycle, and be accountable for the technical design (with support from the lead architect) and end-to-end implementation of the application; with an emphasis on enabling graph databases
- Develop containerized microservices and implement best practices in the reusability of data
- Collaborate in a small squad, as part of a larger team, architecting, extending, maintaining, and tuning our big data capabilities, working with both product and engineering teams to ensure that our systems are fit for purpose
- Drive code quality, CI/CD, and software development lifecycle (SDLC) of the application, and monitor system performance and implement tuning, and accurately estimate and implement feature work to a high standard, meeting both functional and non-functional requirements, on time
- Manage technical debt, making the right calls between balancing pragmatic delivery and compromising implementation patterns, and contribute to technical project meetings, reviews and delivery activities.
Experience we're seeking
- Experience with open source graph databases, e.g. neo4j, ArangoDB, etc.; experience with cloud based graph databases e.g. AWS Neptune, Neo4j Enterprise, etc.
- Working knowledge of graph data science fundamentals, e.g. centrality analysis, community detection, path analysis, partitioning, page rank, shortest path analysis, etc.
- Proficient in converting graph based data structures between true graph objects and other storage formats, e.g. neo4j to json stored in a cloud bucket, and vice versa; knowledge of technical constraints and performance issues in order to understand when to apply each method
- Highly experienced in designing and managing the successful delivery of cloud-native data pipeline applications at scale
- Experienced in SDLC for a major projects
- Discerning the nuances of the business context that should impact on system design and applying the right solution to the problem
- Competent in creating a shared service used by multiple consumers
- Skilled in designing interfaces with particular awareness of whether functionality should be inside or outside a service's boundary
- Proficient in Python, SQL (MySQL and PostgreSQL databases), Spark and Spark SQL
- Experienced across GCP (DataProc, Big Query), AWS (EMR)
- Experienced with containers and orchestration (Docker, Kubernetes)
- Experienced in the ELT paradigm and have worked with a range of tools, experience with Data Bricks is a plus