Principal Data Engineer-AI-21-0162
We are Relativity. A market-leading, global tech company that equips legal and compliance professionals with a powerful platform to organize data, discover the truth, and act on it. The US Department of Justice, 199 of the Am Law 200, and more than 329,000 enabled users trust Relativity during litigation, internal investigations, and compliance projects.
Our SaaS product, RelativityOne, has become the fastest-growing product in the company's history and we have consistently been named a great workplace. As we grow, we continue to seek individuals that will bring their whole, authentic self to our team.
We believe that great talent is not bound by geography and that what you do matters more than where you do it. Relativity has assumed a hybrid work strategy, allowing choice and flexibility for employees to work either from home, a physical Relativity office location (once safe to do so), or a combination of the two, within certain logistical boundaries. Submit your application to learn more from our recruiters or contact us for more details.
About AI at Relativity
In the past two years, billions of documents have already benefited from the insights of Relativity AI – and we are just getting started on our journey to use AI to improve each user experience, product, matter, and investigation at Relativity. We are focused on helping our users discover the truth more quickly, and act on data with confidence.
· We are focused on algorithm excellence, to provide the most robust and trusted experience possible.
· We are creating a world class toolset to solve complex challenges quickly and iteratively.
· AI leveraged everywhere, all stages of discovery to better manage cases and optimize product operations.
As a team, we believe in exploration, experimentation, and bringing your curiosity to work every day. We know that you can’t innovate without experimentation — and a little failure happens on the path to invention. We use the latest and greatest to ensure we are the best. We strive to experiment, ship, and learn every day.
About Data Engineering for AI
Great insights can’t happen without great data, and the best insights come from massive data. Our data infrastructure and engineering ensure that the breadth of Relativity data is available for insights, confidential data is kept confidential, and data is protected at all times. To continue to unlock more insights, we are investing heavily in data pipeline and data lake technology. If you are fluent in big data technologies such as Hadoop/HDFS, Kafka, data pipelines, blob storage, distributed file systems, big data storage formats, Python, Spark, JVM/Scala, Snowflake, and are looking for at-scale challenge with a ton of new innovation and experimentation ahead, you will find yourself at home on the AI data engineering team within Relativity. The team is small but growing fast; you’ll have a huge impact in shaping our direction, what tech we use, and developing best practice. We seek collaborative builders who want to move fast and love a challenge.
About the Principal Data Engineering Role for AI
The Principal Data Engineer for the AI group is a strategic position for all of Relativity. You’ll work both within our team and across the company to leverage our data at scale. You’ll be a company-wide expert on big data storage, pipelines, streaming, micro-batch, and batch technologies. You’ll be partnering directly with our data scientists to create best in class tooling for managing our fleet of models. You’ll inspire software engineers to engage, learn, and focus on big data technologies. You’ll empower our data scientists and data engineers to dream bigger about what’s possible. Innovations that you help create and deliver will be running on Relativity’s global cloud footprint, powering billions of insights. You’ll enjoy your time doing hands-on creation, but also love empowering others via mentorships and coaching.
- Own and facilitate key design decisions related to our big data and data science infrastructure and toolset.
- Lead large initiatives from an architecture perspective via big ideas, sweating the details, and great communication to inspire the team.
- Encourage innovation and data curiosity and the use of data at scale.
- Prove out the use of new technology via compelling proof of concepts and demonstration.
- Collaborate with our data scientists, product managers, and engineering teams to bring ideas from proof of concept to scaled solution.
- Contribute to our technical investments roadmap and help prioritize tech debt and architecture investments.
- Mentor talent within the AI/ML team to promote career development, risk taking, innovation, and create a culture of learning.
- Recruit talent by talking in industry about what we’re building and our innovation.
- Advise senior company and technology leadership on innovative tech for either partnership or acquisition.
- Multiple roles designing APIs, service-oriented architectures, cloud based distributed systems, and big data systems.
- Proven leadership skills and track record of delivering complex technical solutions.
- Experience with product / tool / vendor evaluation and selection.
- Excellent communication skills.
- Experience creating batch and stream processing leveraging technologies like Hadoop/HDFS, Kafka, data pipelines, blob storage, distributed file systems, big data storage formats, SQL, no SQL, Python, Spark, JVM/Scala, and cloud-based data warehouses.
- Experience developing and owning ETL/ELT and data pipelines using a variety of tools.
- Still hands-on as needed, able to drive proof of concepts to completion, and create compelling demos.
- Experience creating processes and systems to manage data quality.
- Fluent in multiple languages, preferably Python and a JVM language.
- Experience in Kubernetes.
- Experience with AWS, Google Cloud, or Azure data infrastructure and tooling
- Experience collaborating with data science teams with conceptual knowledge on data science project lifecycles and techniques.
- Experience designing, building, and managing either data lakes, data marts, and data warehouses.
- Experience training and deploying machine learning models.
- Experience with Azure cloud environment and Azure’s data management and data science toolset.
- Fluent in C# and .Net technologies.