Principal Data Engineer-AI-21-0162
Remote United States
Applications have closed
Relativity
Organizations around the globe use Relativity's secure, end-to-end legal software for their biggest data challenges.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.
Responsibilities:
- 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.
Preferred Qualifications:
- 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
Minimum Qualifications:
- 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.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.
Tags: APIs AWS Azure Big Data Data management Data pipelines Distributed Systems ELT Engineering ETL GCP Google Cloud Hadoop HDFS Kafka Kubernetes Machine Learning ML models Pipelines Python Scala Snowflake Spark SQL Streaming
Perks/benefits: Career development Startup environment
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