Data Integration Developer II

Americo Financial Life and Annuity•Kansas City, MO

About The Position

The Data Integration Developer II designs, builds, and maintains reliable data pipelines and analytical datasets that transform insurance and annuity data into trusted assets for reporting, analytics, and business operations across Americo. The role works with growing independence to translate business requirements into technically sound integration solutions. The developer delivers batch and near-real-time integrations using SQL and an ETL tool, creates curated datasets and data models, and implements validation, reconciliation, and data quality controls. The role also optimizes data processes, documents sources and transformations, tests and deploys solutions, monitors production workflows, and troubleshoots moderately complex defects. It applies team standards, participates in peer reviews, and recommends practical improvements to tools, processes, and data management practices. For assigned pipelines and datasets, the developer selects implementation approaches and modeling strategies, signs off on outputs before release to downstream consumers, and resolves routine data quality issues within approved boundaries. Architectural questions and changes affecting shared production environments require escalation or senior review.

Requirements

Bachelor’s degree in computer science, data engineering, information systems, or a related field, or equivalent hands-on experience.
At least 3 years of professional experience developing and maintaining batch or near-real-time data pipelines, including production implementation of transformations, validations, and error resolution.
Proficiency in SQL, including complex joins, aggregation, and query performance optimization.
Proficiency with Informatica PowerCenter or an equivalent ETL/ELT tool.
Experience with data modeling concepts and curated dataset development for reporting and analytical use cases.
Ability to design, build, and maintain ETL workflows with complex transformations, error handling, restartability, and scheduling.
Ability to write and optimize SQL for complex transformations and analytical use cases and maintain dimensional and relational data models.
Ability to implement validation rules, reconciliation checks, and monitoring logic, and investigate and document anomalies affecting financial, actuarial, and operational data.
Ability to maintain source-to-target mappings, transformation logic, and lineage documentation in accordance with team standards.
Understanding of pipeline monitoring, operational alerting, data reconciliation, schema validation, and basic data observability practices.
Ability to independently translate moderately complex business requirements into reliable, well-documented pipeline solutions and resolve moderately complex pipeline issues.

Nice To Haves

Experience with Americo’s data integration environment.

Responsibilities

Design, build, and maintain batch and near-real-time data pipelines using an ETL tool and SQL to ingest, transform, and deliver insurance and annuity data.
Work with business partners and analysts to clarify requirements, map source data to business concepts, and implement solutions supporting underwriting, policy administration, claims, finance, and in-force management.
Create and maintain curated datasets, data models, and transformation logic for dashboards, operational reporting, and analytical use cases.
Implement validation checks, reconcile outputs, investigate anomalies, and resolve data issues affecting trusted data assets and insurance business processes.
Improve data jobs, queries, and workflows to reduce processing time, improve reliability, and support scalable data delivery.
Document data sources, transformation rules, job schedules, business definitions, source-to-target mappings, and lineage.
Perform unit and integration testing, monitor production processes, and troubleshoot moderately complex data defects.
Apply established development practices, participate in peer reviews, and recommend improvements to tools, processes, and data management approaches.
Implement schema validation, business-rule validation, data quality controls, and approved data contract requirements for assigned data assets.
Select implementation approaches and data modeling strategies for assigned pipelines and datasets; escalate architectural design questions to a senior engineer or Data Architect.
Sign off on assigned pipeline and dataset outputs before releasing them to downstream consumers and independently resolve routine data quality issues within approved boundaries.
Participate in decisions about coding standards and data handling practices, proposing enhancements for senior review when they affect shared production environments.