Data Engineer

Lakeview Loan Servicing is hiring a Data Engineer to build and evolve data systems that support analytics, reporting, AI development, and operational decision-making. The role designs and operates data pipelines, platforms, and curated datasets while partnering with product, engineering, AI, analytics, and business stakeholders.

Responsibilities:

• Design, build, and maintain robust data pipelines for a wide variety of input and output sources, including internal systems, third-party platforms, files, APIs, event streams, and databases
• Develop scalable ETL and ELT workflows for both batch and real-time processing
• Ensure pipelines are reliable, testable, observable, and easy to extend as business needs evolve
• Build reusable data integration patterns that support growing volumes, new source systems, and downstream consumers across analytics, applications, and AI initiatives
• Design and manage data architectures that support OLTP, OLAP, and reporting workloads across operational and analytical environments
• Build and optimize data models, warehouse schemas, and curated datasets for analytics and BI use cases
• Contribute to the design and operation of modern data platforms, including warehouses, lakehouses, streaming systems, and supporting orchestration frameworks
• 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 such as AWS, GCP, or Azure
• Use infrastructure-as-code, CI/CD, and automation practices to improve deployment speed, consistency, and reliability
• Monitor production data systems using logging, alerting, and observability tooling to proactively identify and resolve issues
• Support secure, resilient, and cost-conscious operation of cloud-based data infrastructure
• Implement data quality checks, validation rules, reconciliation processes, and monitoring to ensure trustworthy data across systems
• Establish and maintain standards for lineage, documentation, metadata, schema evolution, and operational runbooks
• Partner with stakeholders to improve data accessibility, consistency, and usability while maintaining appropriate controls and governance
• Contribute to practices that support security, privacy, auditability, and compliance in a regulated environment
• Partner closely with Product, Engineering, and business stakeholders to understand data needs, workflows, and constraints
• Translate business and operational requirements into clean, scalable, and maintainable data solutions
• Support downstream consumers of data, including analysts, researchers, product teams, and operational users
• Communicate clearly with both technical and non-technical stakeholders about data availability, quality, tradeoffs, and delivery timelines
• Continuously improve pipeline performance, reliability, scalability, and developer productivity
• Identify opportunities to simplify architecture, reduce operational toil, and improve data platform leverage across teams
• Operate with a strong bias toward action and iterative delivery, moving quickly from problem definition to implementation and improvement
• 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, including databases, APIs, files, message queues, SaaS platforms, and event streams
• 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