Data & AI Engineer

Our Company

Explore how you can contribute at AmeriLife.

For over 50 years, AmeriLife has been a leader in the development, marketing and distribution of annuity, life and health insurance solutions for those planning for and living in retirement.

Associates get satisfaction from knowing they provide agents, marketers and carrier partners the support needed to succeed in a rapidly evolving industry.

Job Summary

AmeriLife is seeking a Data & AI Engineer to help build, modernize, and scale our enterprise data and artificial intelligence capabilities.

Reporting to the Director of Data Engineering within the Enterprise Data & AI Platforms organization, this individual will play a hands-on engineering role in developing modern data pipelines, integrating enterprise data sources, and driving intelligent automation across the organization.

The role will focus on advancing AmeriLife's Databricks Lakehouse platform, supporting the transition from legacy data environments to scalable, cloud-native architectures, and enabling enterprise analytics, reporting, and automation.

This is a hands-on engineering position requiring exceptional proficiency in Python, PySpark, and SQL, a strong foundation in modern data engineering, and a hyperautomation mindset focused on eliminating manual processes, building reusable engineering frameworks, and improving operational efficiency.

The ideal candidate is a technically curious, execution-oriented engineer who enjoys writing code, solving complex problems, and applying modern technologies to build scalable, automated enterprise data solutions.

Job Description

Key Responsibilities

Enterprise Data Engineering & Databricks

• Design, develop, test, and maintain scalable data ingestion, transformation, and integration pipelines using Databricks, PySpark, Python, and SQL.
• Build and enhance enterprise data pipelines following modern Lakehouse and Medallion Architecture (Bronze, Silver, and Gold) principles.
• Develop reusable, modular, and metadata-driven ETL/ELT frameworks to support enterprise data processing.
• Ingest and integrate structured and semi-structured data from databases, APIs, flat files, cloud storage, and enterprise applications.
• Work with Delta Lake, Unity Catalog, Databricks Workflows, and related platform capabilities.
• Support migration and modernization of legacy SQL Server, SSIS, and traditional data warehouse workloads onto Databricks.
• Optimize data pipelines and transformations for performance, reliability, scalability, and cost efficiency.
• Apply advanced Python, PySpark, and SQL techniques to solve complex data processing and engineering challenges.
• Collaborate with senior engineers and architects to implement enterprise data engineering standards and best practices.

Hyperautomation & Intelligent Engineering

• Apply a hyperautomation-first mindset to identify opportunities for eliminating repetitive manual processes across data engineering and platform operations.
• Develop Python-based automation frameworks, utilities, and reusable engineering components.
• Design and implement metadata-driven and configuration-driven solutions that reduce manual coding and operational dependencies.
• Leverage scripting, APIs, orchestration, and intelligent automation techniques to streamline engineering workflows.
• Explore practical applications of AI-assisted development and intelligent automation to accelerate engineering productivity.
• Continuously identify opportunities to simplify processes, reduce technical debt, and improve engineering efficiency through automation.

Data Integration & Transformation

• Develop efficient data transformation logic using SQL, Python, and PySpark.
• Implement joins, aggregations, business rules, enrichment logic, and incremental processing patterns.
• Work with relational databases, APIs, JSON, Parquet, Delta, and other common enterprise data formats.
• Support dimensional modeling, historical data tracking, and efficient data processing patterns.
• Collaborate with business analysts, data architects, and technical teams to translate requirements into engineering solutions.
• Build scalable and reusable integration components following enterprise engineering standards.

Data Quality, Governance & Reliability

• Implement automated data quality checks covering completeness, accuracy, consistency, uniqueness, and referential integrity.
• Develop reconciliation and validation routines across source systems, transformation layers, and downstream datasets.
• Support enterprise metadata management, data lineage, and cataloging initiatives.
• Apply data governance standards using Unity Catalog and other enterprise governance capabilities.
• Build monitoring, logging, and alerting capabilities for data pipelines and scheduled workloads.
• Troubleshoot pipeline failures, data discrepancies, performance issues, and integration errors.
• Participate in root-cause analysis and implementation of permanent corrective solutions.
• Follow established security, access control, data privacy, and auditability requirements.

Engineering Automation & DevOps

• Develop and maintain engineering code using Git-based version control and collaborative development practices.
• Support automated deployment pipelines using CI/CD frameworks and DevOps tooling.
• Implement unit tests, integration tests, and automated validation for data engineering workloads.
• Create reusable Python utilities, notebooks, SQL scripts, and engineering components.
• Support configuration-driven and metadata-driven pipeline development.
• Contribute to infrastructure automation and environment management practices.
• Document technical designs, data mappings, transformations, deployment procedures, and operational runbooks.
• Continuously identify opportunities to improve engineering productivity through automation and reusable frameworks.

Required Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related technical discipline, or equivalent practical experience.
• 2+ years of hands-on experience in data engineering, software engineering, data integration, or related technical roles.
• Very strong Python programming skills, including scripting, modular development, data processing, and automation.
• Very strong SQL proficiency, including complex queries, joins, aggregations, window functions, transformations, and query performance optimization.
• Strong hands-on PySpark experience, including distributed data processing, DataFrame operations, transformations, and performance optimization.
• Experience developing ETL/ELT pipelines and working with relational databases or modern data platforms.
• Working knowledge of Databricks, Apache Spark, Delta Lake, or comparable cloud-based data engineering technologies.
• Understanding of modern data architecture concepts, including data lakes, data warehouses, lakehouses, and data pipelines.
• Experience working with structured data, JSON, CSV, Parquet, APIs, and other common integration formats.
• Familiarity with cloud platforms such as Microsoft Azure or AWS.
• Basic understanding of data modeling, data quality, and data validation practices.
• Experience using Git or comparable source control tools.
• Strong automation and hyperautomation mindset, with demonstrated ability to simplify workflows, eliminate manual tasks, and build reusable engineering solutions.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to work independently on assigned engineering tasks while collaborating effectively within a broader technical team.
• Strong communication skills and willingness to learn new technologies.

Preferred Qualifications

• Databricks certification is a plus, particularly Databricks Certified Data Engineer Associate or Professional.
• Hands-on experience with Azure Databricks, Delta Lake, Unity Catalog, and Databricks Workflows.
• Experience with Azure Data Factory, Azure Data Lake Storage, Azure DevOps, or comparable cloud engineering services.
• Experience migrating legacy SQL Server, SSIS, stored procedures, or traditional ETL workloads to modern cloud data platforms.
• Experience building reusable automation frameworks, metadata-driven pipelines, or configuration-driven engineering solutions.
• Familiarity with AI-assisted development tools and practical applications of intelligent automation.
• Experience with data orchestration frameworks, automated testing, CI/CD, and deployment automation.
• Understanding of dimensional modeling, Medallion Architecture, Data Vault, and modern enterprise data design principles.
• Experience implementing data quality checks, reconciliation routines, lineage, or metadata-driven frameworks.
• Exposure to real-time or near-real-time data processing, streaming, or event-driven integrations.
• Experience working with insurance, financial services, commissions, policy administration, producer management, or enterprise financial datasets.
• Relevant Azure or AWS certifications are a plus.

Engineering Profile

The ideal candidate combines the mindset of a strong software engineer, modern data engineer, and hyperautomation practitioner. This individual should be highly proficient in Python, PySpark, and SQL, comfortable developing Databricks pipelines, integrating APIs, optimizing transformations, and troubleshooting complex data engineering challenges. Beyond writing code, we are looking for an engineer who consistently challenges manual processes and looks for opportunities to automate, simplify, and scale engineering solutions.

We are looking for someone who is:

• Technically strong: Demonstrates excellent hands-on programming skills in Python, PySpark, and SQL.
• Automation-first: Naturally looks for opportunities to eliminate repetitive work through scripting, reusable frameworks, and intelligent automation.
• Engineering-driven: Values clean code, performance optimization, modular design, automated testing, and maintainability.
• Innovative: Brings curiosity and creativity to solving engineering challenges and improving existing processes.
• Ownership-oriented: Takes responsibility for assigned deliverables and follows through from development to production.
• Collaborative: Works effectively with architects, senior engineers, and cross-functional technology teams.
• Adaptable: Comfortable operating in an evolving technology environment and learning new tools and frameworks.

Success in this role requires strong coding fundamentals, deep technical curiosity, engineering discipline, and a hyperautomation mindset.

The Data & AI Engineer will contribute directly to AmeriLife's enterprise data modernization journey, helping build a modern, scalable, automated, and AI-ready enterprise data platform.

Compensation

• Salary: $110,000
• Salary offers will vary commensurate with experience, education, skills, and training

What AmeriLife Offers

A comprehensive benefits package that includes PTO, medical, dental, vision, retirement savings, disability insurance, and life insurance.

Equal Employment Opportunity Statement

We are an Equal Opportunity Employer and value diversity at all levels of the organization. All employment decisions are made without regard to race, color, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), sexual orientation, gender identity or expression, age, national origin, ancestry, disability, genetic information, marital status, veteran or military status, or any other protected characteristic under applicable federal, state, or local law. We are committed to providing an inclusive, equitable, and respectful workplace where all employees can thrive.

Americans with Disabilities Act (ADA) Statement

We are committed to full compliance with the Americans with Disabilities Act (ADA) and all applicable state and local disability laws. Reasonable accommodations are available to qualified applicants and employees with disabilities throughout the application and employment process. Requests for accommodation will be handled confidentially. If you require assistance or accommodation during the application process, please contact us at HR@AmeriLife.com.

Pay Transparency Statement

We are committed to pay transparency and equity, in accordance with applicable federal, state, and local laws. Compensation for this role will be determined based on skills, qualifications, experience, and market factors. Where required by law, the pay range for this position will be disclosed in the job posting or provided upon request. Additional compensation information, such as benefits, bonuses, and commissions, will be provided as required by law. We do not discriminate or retaliate against employees or applicants for inquiring about, discussing, or disclosing their pay or the pay of another employee or applicant, as protected under applicable law. Pay ranges are available upon request.

Background Screening Statement

Employment offers are contingent upon the successful completion of a background screening, which may include employment verification, education verification, criminal history check, and other job-related inquiries, as permitted by law. All screenings are conducted in accordance with applicable federal, state, and local laws, and information collected will be kept confidential. If any adverse decision is made based on the results, applicants will be notified and given an opportunity to respond.