Data and Quality Engineer

Data & Quality Engineer

Location:
West Des Moines, IA (Preferred) or Sioux Falls, SD

Position Overview

The
Data & Quality Engineer
plays a critical role in ensuring the accuracy, reliability, and integrity of enterprise
Data & Integration platforms
. This role focuses on validating data pipelines, identifying defects, performing root-cause analysis, and collaborating closely with cross-functional teams to continuously improve data quality and integration processes.

The ideal candidate brings a strong foundation in
data quality engineering, testing practices, and data validation
, along with hands-on experience querying and validating data across platforms such as
Salesforce, Snowflake, and other enterprise data stores
. This individual is detail-oriented, analytical, and passionate about delivering high-quality, trusted data solutions.

Key Responsibilities

Data Quality & Validation

• Design and execute comprehensive
  data quality checks
  across data integration platforms.
• Validate data accuracy, completeness, consistency, and timeliness using
  SQL, SOQL, Snowflake
  , and other query languages.
• Validate data across multiple Salesforce objects and external data sources.
• Identify defects, perform
  root-cause analysis (RCA)
  , and collaborate with engineering teams to resolve issues.

Testing & Quality Engineering

• Develop and execute
  test plans, test cases, and validation strategies
  for data and integration workflows.
• Support test automation efforts (
  AccelQ experience is a plus
  ).
• Ensure data quality throughout the lifecycle of ingestion, transformation, and consumption.

Data Integration & Engineering Support

• Investigate and analyze feasibility of data integration prototypes and program requirements.
• Develop specifications and plan programming actions required to produce data integration components.
• Support reporting and analytics infrastructure using
  Kafka, MuleSoft, Salesforce
  , and other integration services.
• Guide internal business partners in developing, troubleshooting, and optimizing
  complex SQL-based reporting and analytics solutions
  .

Platform & Infrastructure Development

• Develop, test, and deploy code using internal development toolsets.
• Support infrastructure and solutions for
  secure data storage, data catalogs, and data query platforms
  .
• Contribute to the development and maintenance of
  data pipeline architecture and infrastructure
  .

Collaboration & Delivery

• Partner with business and technical stakeholders to understand data and reporting requirements.
• Lead complex data initiatives or support larger programs as part of a team.
• Analyze and assess impacts of system changes on downstream programs and integrations.
• Research, evaluate, and propose effective solutions for system enhancements and data improvements.
• Conceptualize and develop new data solutions to meet evolving business needs.

Required & Desired Skills

Highly Desired / Critical Skills

• Salesforce experience creating custom queries using
  SOQL
  and integrating data with external systems.
• Strong understanding of
  Salesforce data models, objects, and data management best practices
  .
• Experience developing, testing, and maintaining
  data pipeline architecture
  .
• Experience with
  data modeling and enterprise data stores
  (training on internal Operational Data Stores provided).
• Strong background in
  test planning, data validation, and quality assurance practices
  .

Additional Experience

• Data analysis and validation across multiple
  data integration platforms
  .
• Experience supporting
  Life & Annuity business applications
  .
• Experience with
  Kafka, MuleSoft
  , and modern integration services.
• Strong SQL skills supporting advanced reporting and analytics use cases.

Experience Requirements

• 5-7 years of experience
  as a Data & Quality Engineer or Data Quality Analyst.
• 2-3 years of hands-on Salesforce experience
  .

Why This Role

• High-impact role supporting enterprise-scale data and integration platforms.
• Opportunity to influence data quality standards and integration best practices.
• Collaborative environment with exposure to modern data technologies.