Principle Data Engineer

Splash Financial•Cleveland, OH

1d•Remote

About The Position

Splash Financial is seeking a highly skilled and experienced Principal Data Engineer to design and implement a scalable, reliable, and high-performance data platform. In this role, you will be responsible for developing data models, architecting robust big data systems, and ensuring scalability and fault tolerance across our data ecosystem. The ideal candidate will have a strong background in data engineering, distributed systems, and cloud infrastructure, with a deep understanding of best practices in data modeling, system architecture, and data governance.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
10+ years of experience in big data architecture and data engineering roles.
Strong proficiency with big data technologies: Hadoop, Spark, Kafka, Hive, Presto, Flink, etc.
Expertise in data modeling, warehousing solutions (e.g.Snowflake, Databricks, BigQuery), and distributed systems.
Hands-on experience with cloud platforms (AWS preferred) and infrastructure-as-code (Terraform).
Knowledge of data privacy and financial data compliance regulations.
Excellent problem-solving and communication skills.
Experience in the lending/credit or broader financial services industry.
Experience using Fivetran, Dbt, Airflow and Snowflake.
Familiarity with MLOps tools (MLflow, Kubeflow) and real-time analytics.

Responsibilities

Design and implement end-to-end big data solutions that meet functional and non-functional requirements, focusing on scalability, reliability, and performance.
Design logical and physical data models for structured, semi-structured, and unstructured data.
Work closely with engineering, data science, product, and infrastructure teams to ensure seamless data flow and system interoperability.
Optimize storage and processing layers for performance and cost-efficiency.
Ensure high availability and disaster recovery for the data platform.
Establish data governance standards including data quality, metadata management, and lineage tracking.
Conduct performance tuning and capacity planning for large-scale data environments.
Evaluate and recommend tools, frameworks, and technologies to enhance the data ecosystem.
Mentor junior data engineers and provide architectural guidance to cross-functional teams.