Big Data Staff Engineer

Job Overview

At Relativity, we make software to help users organize data, discover the truth, and act on it. Our e-discovery platform is used by more than 13,000 organizations around the world to manage large volumes of data and quickly identify key issues during litigation, internal investigations, and compliance projects.

We are seeking a Staff Data Engineer to join our Relativity Data Services organization, a team dedicated to developing data and AI infrastructure that powers AI-driven applications in support of our mission to drive pursuit of justice.

Relativity’s scale and breadth provide significant opportunities for rich data exploration and insights. Our market position and advanced products ensure that our latest models and insights can quickly benefit our users. Great insights stem from excellent data, and the best insights arise from substantial data. Our data infrastructure and engineering guarantee that Relativity's vast data is accessible for insights, confidential data remains secure, and data protection is always upheld. We are making substantial investments in data pipeline and data lake technology for the future.

In this role, you will partner with teams across the Data Services organization to scale and optimize our data platforms, advance tooling for large-scale distributed processing, and enable critical use cases such as reporting, analytics, and audit. Additionally, you will lead efforts to redefine and strengthen our approach to audit and behavioral analytics.

Role overview:

Staff Engineer serves as a technical liaison between his or her teams and other internal and external development teams to identify and resolve dependencies, to identify, improve, and apply software engineering best practices and processes, and to identify and mitigate risks to the on-time delivery of software. Staff Engineer – thinks what to buy or what to build, designs the architecture to serve the user needs and support the system scale. Understands the trade-offs to be made and that there are no silver bullets. But ultimately builds systems that work, deliver value in time and are predictable to operate and extend. Staff Engineer serves as a mentor to other team members to improve technical and process expertise and promotes collaboration.Job Description and Requirements

Responsibilities

• Lead design of software using abstraction, low coupling and high cohesion, modularization, encapsulation and information hiding, and separation of concerns.
• Lead implementation of software using practical application of algorithms, defensive programming and exception handling, fault tolerance, design patterns. Be pragmatic – in using object-oriented principles, applying SOLID principles and design patterns in a variety of languages.
• Build systems that are low maintenance but not overengineered – balancing security, observability and extensibility with time-to-market and user value.
• Specify non-functional software requirements and analyze all requirements to determine design feasibility within time and cost constraints.
• Test and lead test of software emphasizing the practice of Test-Driven Design and the use of autonomous frameworks and Continuous Integration.
• Identify and offer solutions to reduce technical debt.
• Display an ownership mindset; be accountable for and beyond the features your team and larger organization develops.
• Provide solutions to varied and ambiguous issues, utilizing judgment to select methods and techniques for obtaining solutions.
• Offer coaching to ensure the team stays focused and delivers against the goals, adapting to changing business requirements.
• Advocate for and ensure adherence to best practices in coding standards, quality assurance, and security, while aligning solution with company-wide architectural principles.

Your Skills

• 10+ years of professional software development experience on commercial-grade systems and applications with a proven track record of building and shipping successful software. 
• 6+ years of hands-on experience with large-scale data infrastructure and cloud-native distributed systems.
• Proven proficiency in multiple programming languages, with a strong aptitude for rapidly learning and adapting to new technologies. Experience with at least two of the following is required: Java, Python, Scala, Rust and C#.
• Experience building and optimizing data pipelines using Apache Spark for large-scale batch workloads.
• Familiarity with deploying and managing data workloads on Kubernetes, including containerization and orchestration best practices.
• Extensive knowledge of and adherence to SDLC (Software Development Life Cycle) standards and best practices.
• Ability to consistently identify and deliver technical improvement feedback to team members in a supportive and constructive manner, to achieve demonstrable results over time.
• Excellent problem solving-solving skills, with a clear ability to present trade-offs, make informed decisions, and drive strategic execution.
• Excellent verbal and written communication to clearly, succinctly, and completely communicate intent (both technical and non-technical) in interactions with team members and management.
• Nice to have: Experience working with Data Lake and Lakehouse architectures on cloud storage platforms like ADLS.
• Nice to have: Hands-on experience or practical understanding of machine learning systems and their integration into production environments.

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. 

#LI-MM5

Relativity is committed to competitive, fair, and equitable compensation practices.

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

300 000 and 450 000PLNThe final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.