Lead Data Engineer

Bayview Asset Management, LLC is seeking a Lead Data Engineer for their Nebula team, responsible for shaping and scaling the data foundation that supports analytics and operational decision-making. This role combines hands-on data engineering with team leadership, focusing on building reliable data systems and mentoring a team of data engineers.

Responsibilities:

• Own the architecture and evolution of core data systems, including ingestion, transformation, orchestration, storage, modeling, and delivery layers
• Set technical direction for ETL/ELT, batch processing, real-time pipelines, OLTP and OLAP systems, and BI-ready data assets
• Make pragmatic architecture decisions that balance scalability, reliability, security, performance, cost, and delivery speed
• Establish engineering standards, reusable patterns, and design principles that improve quality and leverage across the data platform
• Lead the design, build, rollout, and operations of greenfield data infrastructure
• Build and maintain complex data pipelines across diverse source and destination systems, including databases, APIs, files, SaaS platforms, event streams, and internal applications
• Design and optimize data models, warehouse schemas, semantic layers, and curated datasets for analytics, reporting, AI, and product use cases
• Contribute directly to critical implementation work, including writing code, code and design reviews, migrations, reliability improvements, and production issue resolution
• Lead a lean, high-caliber squad of data engineers, spending focused time mentoring, coaching, managing, and coordinating the team
• Develop engineers through regular feedback, technical guidance, code reviews, career support, and clear expectations around quality and ownership
• Help prioritize team work, clarify scope, remove blockers, and ensure the squad delivers reliably against business and technical goals
• Contribute to hiring, onboarding, performance development, and team operating rhythms as the data engineering function grows
• Deploy, operate, and improve data pipelines, data stores, and supporting infrastructure on major cloud platforms such as AWS, GCP, or Azure
• Drive strong practices for CI/CD, infrastructure-as-code, automated testing, monitoring, alerting, and incident response
• Ensure data systems are observable, fault-tolerant, recoverable, and maintainable in production
• Identify opportunities to reduce operational toil, improve platform reliability, and manage cloud infrastructure costs effectively
• Define and enforce standards for data quality, validation, reconciliation, lineage, schema evolution, metadata, and documentation
• Establish patterns for data contracts, ownership, SLAs, and runbooks that help downstream teams trust and use data confidently
• Partner with security, compliance, and business stakeholders to support privacy, auditability, access controls, and regulated data handling
• Raise the maturity of data governance and reliability practices without slowing down pragmatic delivery
• Partner closely with Product, Engineering, AI, Analytics, and business stakeholders to align data architecture with organizational priorities
• Translate ambiguous business needs and operational workflows into clear technical plans, milestones, and production-ready solutions
• Serve as a senior technical point of contact for data-heavy initiatives, communicating tradeoffs, risks, sequencing, and timelines clearly
• Enable downstream consumers, including analysts, product teams, data scientists, and operational users, through reliable and well-modeled data assets
• Contribute to a culture of ownership, curiosity, operational rigor, pragmatism, and engineering excellence
• Raise the bar for the team through thoughtful design, clear abstractions, strong reviews, and sound technical judgment
• Balance staff-level technical depth with practical people leadership, helping the team grow while continuing to ship high-quality systems

Requirements:

• 5-8+ years of experience building and operating production-grade data pipelines, platforms, and distributed data systems
• 2+ years of experience leading, mentoring, or managing data engineers in a tech lead, staff-level project lead, engineering manager, or TLM capacity
• Strong hands-on experience with industry-standard tools and platforms for ETL/ELT, orchestration, data warehousing, streaming, and BI
• Deep understanding of OLTP and OLAP systems, including the ability to design architectures that support transactional, analytical, and operational workloads
• Experience building flexible data pipelines across many source and destination types, including databases, APIs, files, queues, event streams, SaaS platforms, and internal systems
• Strong experience with both batch and real-time processing patterns, including tradeoffs in latency, reliability, cost, and operational complexity
• Experience deploying and operating cloud-based data infrastructure on AWS, GCP, or Azure
• Advanced SQL and data modeling expertise, including schema design, warehouse optimization, semantic modeling, and performance tuning
• Strong programming ability in languages commonly used in data engineering, such as Python, Java, Scala, Go, or similar
• Comfort with CI/CD, infrastructure-as-code, automated testing, observability, incident response, and production operations for data systems
• Strong architectural judgment in ambiguous environments where systems must balance speed, reliability, compliance, maintainability, and long-term leverage
• Clear communication skills with both technical and non-technical teammates, including the ability to explain tradeoffs and influence direction
• Experience operating as a Technical Lead or Tech Lead Manager responsible for both technical implementation, technical direction, and people development
• Experience with modern orchestration and transformation tools such as Airflow, Dagster, dbt, or similar platforms
• Experience with cloud-native warehouses or lakehouse platforms such as Snowflake, BigQuery, Redshift, Databricks, or equivalent technologies
• Experience with streaming systems such as Kafka, Kinesis, Pub/Sub, Flink, Spark Streaming, or similar technologies
• Experience enabling BI and self-service analytics through curated datasets, semantic layers, and reporting platforms such as Looker, Tableau, Power BI, or similar tools
• Experience building data platforms that support AI, machine learning, decisioning, or LLM-powered workflows
• Experience scaling a data engineering function, including technical standards, operating rhythms, hiring, onboarding, and team development
• Experience in fintech, mortgage, lending, payments, insurance, or other regulated domains