Data Integration Specialist

Advantage Tech; Kansas City (KS); Sur place; USD 90 000 - 120 000; Plein temps

Résumé du poste

Advantage Tech is seeking a Data Integration Developer II to design, build, and maintain data pipelines for insurance and annuity data. You will translate business requirements into robust integration solutions that support reporting, analytics, and business operations.

You will develop batch and near-real-time ETL workflows, ensure data quality, and optimize performance while documenting sources, rules, and lineage for auditable assets.

Qualifications

• Bachelor's degree in computer science, data engineering, information systems, or related field or equivalent hands-on experience.
• 3+ years of professional experience building batch or near-real-time data pipelines with ETL/ELT.
• Proficient in SQL, including joins, aggregation, and performance optimization.
• Proficient in Informatica PowerCenter or equivalent ETL/ELT tool; data integration environment experience preferred.
• Experience with data modeling concepts and curated datasets for reporting/analytics.
• ETL/ELT pipeline engineering: design, build, maintain, error handling, scheduling; resolve pipeline issues.
• SQL and data modeling: write/optimize SQL for transformations; build dimensional/relational data models for reporting.
• Data quality and reconciliation: implement validation, reconciliation checks, anomaly investigation.
• Documentation and lineage: maintain source-to-target mappings and lineage across assets.
• Data observability: monitor pipelines, alerts, reconciliation, schema validation.

Responsabilités

• Design, build, and maintain batch and near-real-time data pipelines.
• Translate business data needs into technical integration solutions for underwriting and policy operations.
• Prepare curated datasets and transform logic for dashboards and reporting.
• Ensure data quality through validation, reconciliation, and issue investigation.
• Optimize pipelines for performance and scalability; monitor and troubleshoot production issues.
• Document data sources, transformation rules, and lineage for auditable assets.
• Support testing, deployment, and issue resolution; participate in peer reviews and standards.

Connaissances

SQL

ETL pipelines

Data modeling

Data quality

Documentation

Problem solving

Communication

Formation

Bachelor's degree

Outils

Informatica PowerCenter

Description du poste

We are looking for a Data Integration Developer II

Job Summary:

Exists to design, build, and maintain reliable data pipelines and analytical datasets that transform insurance and annuity data into trusted assets for reporting, analytics, and business operations. This role operates with growing independence, translating business requirements into technically sound integration solutions.

Key Responsibilities

• Develop reliable data pipelines: Design, build, and maintain batch and near-real-time data pipelines in ETL Tool and SQL that ingest, transform, and deliver insurance and annuity data accurately and efficiently for reporting, analytics, and downstream applications.
• Translate business data needs into technical solutions: Work directly with business partners and analysts to clarify requirements, map source data to business concepts, and implement integration solutions that support underwriting, policy administration, claims, finance, and in-force management.
• Model and prepare data for analysis: Create and maintain curated datasets, data models, and transformation logic that improve data usability, consistency, and performance for dashboards, operational reporting, and analytical use cases.
• Ensure data quality and integrity: Implement validation checks, reconcile outputs, investigate anomalies, and resolve data issues to ensure trusted data assets and accurate information across key insurance business processes.
• Optimize data processes for performance: Identify and implement improvements to data jobs, queries, and workflows to reduce processing time, improve reliability, and support scalable delivery of data products.
• Document technical solutions: Produce and maintain clear documentation for data sources, transformation rules, job schedules, and business definitions so solutions are auditable, supportable, and understandable by peers.
• Support testing, deployment, and issue resolution: Execute unit and integration testing, monitor production processes, and troubleshoot moderately complex data defects to minimize disruption to business operations.
• Contribute to team standards: Apply established development practices, participate in peer reviews, and recommend practical improvements to tools, processes, and data management approaches.
• Implement data quality and contract controls: Implement schema validation, business rule validation, data quality controls, and approved data contract requirements for assigned data assets.

Experience and Qualifications

• Bachelor's degree in computer science, data engineering, information systems, or a related field, or equivalent hands-on experience.
• 3+ years of professional experience developing and maintaining batch or near-real-time data pipelines, including production implementation of transformations, validations, and error resolution.
• Proficient in SQL, including complex joins, aggregation, and query performance optimization.
• Proficient in Informatica PowerCenter or equivalent ETL/ELT tool; experience with data integration environment preferred.
• Experience with data modeling concepts and curated dataset development for reporting and analytical use cases.
• ETL/ELT pipeline engineering: Proficiently designs, builds, and maintains batch and near-real-time ETL Tool workflows including complex transformations, error handling, restartability, and scheduling; independently resolves moderately complex pipeline issues.
• SQL and data modeling: Writes and optimizes SQL for complex data transformations and analytical use cases; builds and maintains dimensional and relational data models that serve reporting and analytics across insurance domains.
• Data quality and reconciliation: Implements validation rules, reconciliation checks, and monitoring logic for pipeline outputs; investigates and documents anomalies affecting financial, actuarial, and operational data.
• Documentation and lineage: Maintains complete source-to-target mappings, transformation logic, and lineage documentation for owned assets; applies team standards consistently across all deliverables.
• Data observability: understand pipeline monitoring, operational alerting, data reconciliation, schema validation, and basic observability practices used to maintain production data assets

Decision Making Authority

• Selects implementation approach and data modeling strategy for assigned pipelines and datasets; escalates architectural design questions to a senior engineer or Data Architect.
• Signs off on assigned pipeline and dataset outputs before releasing to downstream consumers; resolves routine data quality issues independently within approved boundaries.
• Participates in decisions about coding standards and data handling practices; proposes enhancements but requires senior review before implementing changes that affect shared production environments.

Values in Practice

• Accountability: Owns the full delivery lifecycle for assigned pipelines — from requirements through testing and production monitoring — without requiring close oversight.
• Excellence: Produces well-documented, well-tested integration solutions that downstream analytics teams can rely on without manual reconciliation or workarounds.
• Continuous Improvement: Proactively identifies performance issues or recurring data quality patterns in owned pipelines and implements improvements that reduce incidents or processing time.

Leveling Signals

• Independently translates a moderately complex business requirement into a reliable, well-documented pipeline solution without requiring senior engineer involvement in the design phase.
• Consistently produces pipeline outputs that pass validation checks and are adopted into recurring reporting without material data quality follow-up from analysts or business stakeholders.
• Identifies a recurring performance or quality issue in owned work, proposes a solution independently, and implements the fix with standard review — rather than waiting for the issue to elevate.