Consultant, Data Engineer | Snowflake Data Engineer (Python / Snowpark)

Nationwide•Columbus, OH

1d•$118,000 - $178,000•Hybrid

About The Position

Nationwide’s industry-leading workforce is passionate about creating data solutions that are secure, reliable and efficient in support of our mission to provide extraordinary care. Nationwide embraces an agile work environment and collaborative culture through the understanding of business processes, relationship entities and requirements using data analysis, quality, visualization, governance, engineering, AI engineering, and machine learning to produce targeted data solutions. If you have the drive and desire to be part of a future-forward data-enabled culture, we want to hear from you! As a Data Engineer, you’ll be responsible for acquiring, curating, and publishing data for analytical or operational uses. Data should be in a ready-to-use form that creates a single version of the truth across all data consumers, including business users, data scientists, and Technology. Ready-to-use data can be for both real-time and batch data processes and may include unstructured data. Successful data engineers have the skills typically required for the full lifecycle of software engineering development from translating requirements into design, development, testing, deployment, and production maintenance tasks. You’ll have the opportunity to work with various technologies including big data, relational and SQL databases, unstructured data technology, and programming languages.

Requirements

Strong experience in data engineering focused specifically on Snowflake architecture and implementation.
Advanced SQL, Python (including PySpark or Snowpark), and modern transformation tooling like dbt.
Deep knowledge of Role-Based Access Control (RBAC), zero-copy cloning, time travel, and performance tuning.
Hands-on experience within major cloud environments like AWS, Azure, or Google Cloud Platform (GCP).
Six to ten years of relevant experience with data quality rules, data management organization/standards and practices.
Solid experience with software development on large and/or concurrent projects.
Experience in data warehousing, statistical analysis, data models, and queries.
One to three years’ experience with developing compelling stories and visualizations.
Advanced skills with modern programming and scripting languages (e.g., SQL, R, Python, Spark, UNIX Shell scripting, Perl, or Ruby).
Strong problem-solving, verbal and written communication skills.
Ability to influence, build relationships, negotiate and present to senior leaders.

Nice To Haves

Databricks, Apache Spark, PySpark, Apache Iceberg, Unity Catalog, or Snowflake/Databricks interoperability.
Insurance/financial services industry knowledge a plus.

Responsibilities

Lead the design of enterprise-scale data warehousing and cloud integration strategies on Snowflake.
Establish standards for data ingestion, transformation, orchestration, and continuous performance tuning.
Build scalable ELT pipelines leveraging native Snowflake features like Snowpipe, Streams, Tasks, and Snowpark.
Implement data modeling frameworks (Dimensional, Data Vault, or hybrid schemas) using tools like dbt (data build tool).
Optimize query performance and manage warehouse compute costs through effective clustering, caching, and sizing.
Integrate orchestration tools (such as Apache Airflow or Prefect) to manage complex dependency graphs and scheduling.
Consults on complex data product projects by analyzing moderate to complex end-to-end data product requirements and existing business processes to lead the design, development and implementation of data products.
Responsible for producing data building blocks, data models, and data flows for varying client demands such as dimensional data, standard and ad hoc reporting, data feeds, dashboard reporting, and data science research and exploration.
Translates business data stories into a technical story breakdown structure and work estimate so value and fit can be assessed for a schedule or sprint.
Responsible for applying secure software and systems engineering practices throughout the delivery lifecycle to ensure our data and technology solutions are protected from threats and vulnerabilities.
Creates business user access methods to structured and unstructured data by using techniques such as mapping data to a common data model, NLP, transforming data as necessary to satisfy business rules, AI, statistical computations, and validation of data content.
Builds data cleansing, imputation, and common data meaning and standardization routines from source systems by understanding business and source system data practices and by using data profiling and source data change monitoring, extraction, ingestion, and curation data flows.
Facilitates medium- to large-scale data engineering using cloud technologies – Azure and AWS (i.e. Redshift, S3, EC2, Data-pipeline and other big data technologies).
Collaborates with the enterprise DevSecOps team and other internal organizations on CI/CD best practices experience and use of JIRA, Jenkins, Confluence, etc.
Implements production processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it.
Develops and maintains scalable data pipelines for both streaming and batch requirements and builds out new API integrations to support continuing increases in data volume and complexity.
Writes and performs data unit/integration tests for data quality. With input from business requirements and stories, creates and executes test data and scripts to validate that quality and completeness criteria are satisfied. Creates automated testing programs and reusable test data for future code changes.
Practices code management and integration with engineering Git principle and practice repositories.
Participates as an expert, leader, and learner in team tasks for data analysis, architecture, application design, coding, and testing practices.
Leads design decisions for complex data pipelines, integration patterns, and reusable engineering components within a product, platform, or domain.
May perform other responsibilities as assigned.