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About the Role
The Data Engineer on the Nebula team is responsible for building and evolving the data foundation that powers analytics, reporting, AI development, and operational decision-making. This role involves designing, building, and maintaining reliable, scalable, and flexible data systems, partnering with various teams to translate business needs into production-ready data pipelines and platforms.
Responsibilities
- Design, build, and maintain robust data pipelines for a wide variety of input and output sources.
- Develop scalable ETL and ELT workflows for both batch and real-time processing.
- Ensure pipelines are reliable, testable, observable, and easy to extend.
- Build reusable data integration patterns.
- Design and manage data architectures that support OLTP, OLAP, and reporting workloads.
- Build and optimize data models, warehouse schemas, and curated datasets.
- Contribute to the design and operation of modern data platforms.
- Help define patterns for data storage, partitioning, performance optimization, retention, and lifecycle management.
- Deploy, operate, and improve data pipelines and data stores on major cloud platforms.
- Use infrastructure-as-code, CI/CD, and automation practices.
- Monitor production data systems using logging, alerting, and observability tooling.
- Support secure, resilient, and cost-conscious operation of cloud-based data infrastructure.
- Implement data quality checks, validation rules, reconciliation processes, and monitoring.
- Establish and maintain standards for lineage, documentation, metadata, schema evolution, and operational runbooks.
- Partner with stakeholders to improve data accessibility, consistency, and usability.
- Contribute to practices that support security, privacy, auditability, and compliance.
- Partner closely with Product, Engineering, and business stakeholders to understand data needs.
- Translate business and operational requirements into clean, scalable, and maintainable data solutions.
- Support downstream consumers of data.
- Communicate clearly with both technical and non-technical stakeholders.
- Continuously improve pipeline performance, reliability, scalability, and developer productivity.
- Identify opportunities to simplify architecture, reduce operational toil, and improve data platform leverage.
- Operate with a strong bias toward action and iterative delivery.
- Help raise the bar on engineering quality through thoughtful design, testing, documentation, and operational discipline.
Requirements
- 2-4+ years of experience building and operating production-grade data pipelines and data systems.
- Strong experience with industry-standard tools and platforms for ETL/ELT, orchestration, data warehousing, streaming, and BI.
- Experience working with both OLTP and OLAP systems, with a strong understanding of the tradeoffs between transactional and analytical workloads.
- Experience building flexible data pipelines that integrate with many different source and destination types.
- Experience supporting both batch and real-time data processing patterns.
- Experience deploying and operating data infrastructure on major cloud platforms such as AWS, GCP, or Azure.
- Strong SQL skills and experience with data modeling, transformation frameworks, and performance optimization.
- Experience building AI-powered capabilities on top of LLMs, including orchestration, evaluation, and data integration patterns.
- Experience with modern programming languages commonly used in data engineering, such as Python, Java, Scala, or Go.
- Comfort working with CI/CD, infrastructure-as-code, observability, and production operations for data systems.
- Strong judgment in ambiguous environments where requirements evolve and systems must balance speed, reliability, and flexibility.
- Clear communication skills with both technical and non-technical teammates.
- Experience with modern orchestration and transformation tools such as Airflow, Dagster, dbt, or similar platforms.
- Experience with cloud-native data warehouses or lakehouse platforms such as Snowflake, BigQuery, Redshift, Databricks, or equivalent technologies.
- Experience with streaming and real-time data platforms such as Kafka, Kinesis, SQS, or similar systems.
- Experience enabling BI and self-service analytics through curated datasets, semantic layers, and reporting platforms such as Looker, Power BI, Tableau, or similar tools.
- Experience in fintech, mortgage, lending, payments, insurance, or other regulated domains.
- Experience building data platforms that support AI, machine learning, or decisioning workflows.
- Experience improving data quality, reliability, cost efficiency, and platform scalability as a system grows.
Skills
- ETL/ELT
- Orchestration
- Data Warehousing
- Streaming
- BI
- OLTP
- OLAP
- SQL
- Data Modeling
- Performance Optimization
- Python
- Java
- Scala
- Go
- CI/CD
- Infrastructure-as-code
- Observability
- Production Operations
- Airflow
- Dagster
- dbt
- Snowflake
- BigQuery
- Redshift
- Databricks
- Kafka
- Kinesis
- SQS
- Looker
- Power BI
- Tableau
- LLMs
Location
- Remote
Work Type
- Fully remote
Experience Level
- 2-4+ years
Salary/Compensations
- $140,000 to $190,000 USD
Benefits
- Annual bonus
- Medical coverage starting on day one
- Company-matched 401(k)
About the Company
- Bayview is an Equal Employment Opportunity employer.
Equal Opportunity
- All aspects of consideration for employment and employment with the Company are governed on the basis of merit, competence and qualifications without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, or any other category protected by federal, state, or local law.