דרושים Data Architect ב-Mongodb ב-United States, Austin

MongoDB is growing rapidly and seeking a Data Architect to be a key member of a growing Data Architecture team; overseeing the governance, management and stewardship of data across the organization. As a Data Architect, you will be the bridge between the business and data engineering teams. You will design data models and schemas to produce high-performing, curated datasets in a mixed SQL and NoSQL environment, and drive the implementation to completion. You will populate an enterprise data catalog with key technical and business metadata driving data discoverability, and facilitate the adoption of enterprise data governance policies and procedures.

This role will be based remotely in Austin, New York City, Seattle, or California.

• At least 3 years experience building data models to support structured and semi-structured product data including telemetry
• Highly proficient in SQL. MQL (MongoDB Query Language) knowledge is a plus
• Experienced with at least one distributed query engine or cloud-based data warehouse, such as Trino or BigQuery
• Customer focused, understanding that delivering business value is of utmost importance
• Skilled at eliciting requirements and communicating across a diverse set of engineers and partners
• Familiarity with Python and rudimentary Prompt Engineering
• Well-versed in core data lake, data warehousing and data security concepts
• Familiar with data catalog functionality, business glossaries, metadata management and data lineage
• Familiar with cloud computing concepts and big data storage formats such as Parquet and Iceberg
• Capable of juggling multiple projects simultaneously
• A lifelong learner who strives for continuous improvement

As a Data Architect, you will:

• Partner with Product Owners and other Stakeholders to document data requirements, utilizing your experience to provide insights and add value to the process
• Validate and assess incoming product telemetry data, ensuring alignment with documentation and meeting Product Owners and other Stakeholders' expectations by thorough cross-referencing and testing
• Design data models and schemas within the internal data lake and data warehouse that will be used for analytical and data science consumption
• Create and execute test plans to ensure data is accurate and performance meets SLAs
• Scope and create SQL-based pipelines prioritizing data consistency and accuracy
• Implement data security standards for data access
• Document metadata and lineage in the enterprise data catalog, promoting self-service data discovery and exploration

• In 3 months, you’ll have fully ramped with our Business and Data teams
• In 6 months, you’ll have delivered at least one fully documented data model that provides value to the business
• In 12 months, you’ll have delivered multiple data models across a variety of use cases, and are comfortable working with all aspects of our internal data platform

As a Staff Engineer on the Atlas Data Federation and Archiving team, you will lead the design, optimization, and scalability of our storage and federated query systems. This role focuses on high-performance distributed storage, data lifecycle management, and efficient data retrieval at scale.

This role can be based in New York City, Austin, San Francisco, Seattle, or remotely in the United States.

Storage & Data Processing Performance

• Architect and optimize large-scale storage solutions for federated data access, ensuring efficient retrieval, indexing, and query performance
• Optimize data archival pipelines for high-throughput ingestion, durability, and cost-efficiency
• Improve data tiering and lifecycle policies for moving and querying data efficiently across hot, warm, and cold storage tiers
• Reduce operational costs through intelligent storage layout, compaction strategies, and query execution optimizations

Distributed Query & Execution Engine

• Improve and scale our distributed query execution engine, optimizing it for multi-source federated queries and data lake processing
• Enhance query performance across object storage (e.g., S3, GCS, Azure Blob) by optimizing indexing, partitioning, and compaction techniques
• Implement workload-aware autoscaling for query execution and data processing
• Reduce incident rates by improving system resilience, failover mechanisms, and observability

Technical Leadership & Mentorship

• Guide architectural decisions and lead design reviews across engineering teams
• Mentor engineers in distributed systems, data storage optimization, and operational excellence
• Partner with Product Management to define the technical roadmap for storage and data federation solutions
• Participate in on-call rotation, providing senior oversight for incident response and postmortem retrospectives

• 10+ years experience in software engineering, with a focus on backend and distributed storage systems
• Expertise in large-scale storage systems, such as distributed databases, cloud object storage (S3, Azure Blob, GCS), or data lake technologies (Iceberg, Delta Lake, Hudi, etc.)
• Strong background in designing and optimizing storage layers, indexing, and data lifecycle management
• Experience optimizing query engines for high-volume, low-latency federated data access
• Track record of improving system reliability, observability, and cost-efficiency
• Experience with Kubernetes-based deployment of distributed storage or query systems
• Proficiency in Go or Java (preferred, but not required)
• Deep understanding of query optimizers, storage formats (Parquet, ORC), and indexing strategies
• Experience with disaggregated storage and cloud-native data lake solutions
• Proven ability to lead technical initiatives as an individual contributor while mentoring senior engineers and driving technical excellence within a team.