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- 8+ years of experience in AWS Data Engineering
- Expert level experience with cloud data platforms, frameworks, and Lakehouse technologies (e.g. AWS, Databricks, Snowflake, Spark, Delta Lake/Iceberg)
Proven stakeholder communication skills to clearly convey status, risks, architecture decisions, and trade-offs to both technical and nontechnical audiences; influences outcomes without formal authority.
Supports maturing Nationwide IT capabilities and promotes reusable processes and work products; demonstrates standardized automation, secure data practices, and quality routines in data pipelines and information products
Facilitates medium to large-scale data using cloud technologies – Azure and AWS (i.e. Redshift, S3, EC2, Data-pipeline and other big data technologies).
Collaborates with enterprise DevSecOps team and other internal organizations on CI/CD best practices experience using JIRA, Jenkins, Confluence etc.
Technical Skills:
Amazon Web Services
Data Bricks
Data Engineering
Data Management
Data Pipelines
Data Quality
Extract Transform Load (ETL)
Programming Skills
Business Acumen
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Job Description Summary
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, robotic process automation, 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 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 from big data, relational and SQL databases, unstructured data technology, and programming languages.
Job Description
Required Experience & Capabilities:
Successful history of end-to-end data solution delivery, with first-hand experience scoping, sizing, and leading complex engineering initiatives, accountable for milestones, SLAs, and production outcomes.
Expert level experience with cloud data platforms, frameworks, and lakehouse technologies (e.g. AWS, Databricks, Snowflake, Spark, Delta Lake/Iceberg). • Hands-on building robust data pipelines using tools such as Spark, Informatica, or native solutions such as Delta Live Tables, or Snowpark. With experience implementing robust data quality and observability practices defines SLOs and data contracts, implements data validation, profiling, anomaly detection, lineage, and alerting.
Strong software engineering in Python or similar. Applies TDD, modular design, and rigorous code reviews to produce maintainable, reusable components.
Implements and matures DevOps practices for data. Builds automated testing, environment strategies, versioned data, and pipeline releases using infrastructure as code, and utilizes modern automated deployment patterns
Demonstrated leadership and mentoring: uplifts team technical and soft skills, establishes engineering standards/patterns.
Ongoing innovation: identifies and evaluates opportunities to apply DataOps accelerators, and Generative AI to improve developer productivity (e.g., code/test generation), data curation, and delivery efficiency.
Proven stakeholder communication skills to clearly convey status, risks, architecture decisions, and trade-offs to both technical and nontechnical audiences; influences outcomes without formal authority.
Supports maturing Nationwide IT capabilities and promotes reusable processes and work products; demonstrates standardized automation, secure data practices, and quality routines in data pipelines and information products.
Key Responsibilities:
Consults on complex data product projects by analyzing moderate to complex end to end data product requirements and existing business processes to lead in 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 & exploration.
Translates business data stories into a technical story breakdown structure and work estimate so value and fit 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 such techniques 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 using cloud technologies – Azure and AWS (i.e. Redshift, S3, EC2, Data-pipeline and other big data technologies).
Collaborates with enterprise DevSecOps team and other internal organizations on CI/CD best practices experience using 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 a business requirements/story, creates and executes testing data and scripts to validate that quality and completeness criteria are satisfied. Can create automated testing programs and data that are re-usable for future code changes.
Practices code management and integration with engineering Git principle and practice repositories.
Participates as an expert and learner in team tasks for data analysis, architecture, application design, coding, and testing practices.
May perform other responsibilities as assigned.
Qualifications & Experience:
Education:
Undergraduate studies in computer science, management information systems, business, statistics, math, a related field or comparable experience and education strongly preferred. Graduate studies in business, statistics, math, computer science or a related field are a plus.
Experience:
Five to eight 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 distinctive visualizations.
Technical Skills:
Amazon Web Services
Data Engineering
Data Management
Data Pipelines
Data Quality
Extract Transform Load (ETL)
Programming Skills
Business Acumen
Soft Skills & Attributes:
Data application and practice knowledge
Strong communication skills
Ability to influence, negotiate, and set priorities
Mentoring and coaching abilities to guide associates
Identifying and removing blockers to progress
Ability to work in agile and global environments
Insurance/financial services industry knowledge a plus
Other criteria, including leadership skills, competencies and experiences may take precedence
Additional Information:
Reporting Structure:
Reports to Technology or Data Leader – India
Working Hours:
Aligned to India business hours with flexibility for global collaboration.
Location Flexibility:
[Hybrid/Remote/In-office] with periodic in-office collaboration.
Equal Opportunity Statement:
Nationwide is committed to diversity and inclusion. All qualified applicants will be considered without regard to race, gender, disability, religion, or other protected categories.
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