Software Developer – Data & Analytics | COUNTRY Financial

Location: Multiple Locations (U.S.) | Work Mode: Full-time | Compensation: $78,800 – $108,350 | Posting date/time : February 14, 2026

Job Description

Role Summary

COUNTRY Financial is looking for a Data-Focused Software Developer to join our growing analytics team. In this role, you will contribute to critical technology efforts by designing and implementing software solutions that drive business partner priorities. You will serve as a technical expert, guiding projects from conceptual design through implementation and scaling.

Key Responsibilities:

• Participate in the full software development life cycle (SDLC), including requirements analysis and technical specification design.
• Design, develop, and implement large-scale proprietary software solutions for internal data use.
• Identify and evaluate new technologies for implementation within the data space.
• Analyze system requirements and business processes to determine technical requirements and prototype components.
• Support, maintain, and debug software functionality, performing root cause analysis on program errors.
• Consult with end-users to refine and test components to meet specific business needs.

Qualifications & Key Skills:

• Experience: Typically requires 4+ years of relevant experience in software development or data engineering.
• Technical Acumen: Strong proficiency in Python/PySpark and writing complex SQL queries.
• Big Data Knowledge: Understanding of Data Lakes, Business Intelligence (BI) tools, and hosted data platforms.
• Preferred: Familiarity with the Cloudera Data Platform (CDP).
• Soft Skills: Ability to self-manage, meet deadlines, and articulate technical risks to stakeholders.

Job details made easy by trendtoday360

Position: Data Engineer / Software Developer – Analytics Infrastructure
Industry Focus: Insurance, Financial Services, Data Management
Core Technology: PySpark, SQL, Data Lakes, Cloudera

✅ Job Summary and Focus:

This role at COUNTRY Financial is for a Big Data Orchestrator. Because the insurance industry relies heavily on risk assessment and actuarial data, the focus here is on Data Velocity and Integrity. You aren’t just building “apps”; you are building the pipelines and proprietary tools that allow raw data to be transformed into actionable insights. The technical focus is heavily centered on Distributed Computing (PySpark) and Data Lake Architecture. You will be tasked with ensuring that large-scale datasets move efficiently through the Cloudera Data Platform, providing the technical backbone for the company’s most important digital transformation efforts.

📌 Key ATS Resume Keywords:

Software Developer, Data Engineer, PySpark, Python, SQL, Big Data, Data Lake, Cloudera Data Platform, CDP, SDLC, Business Intelligence, Requirements Analysis, Data Integration, Distributed Computing, Insurance Technology, Analytics Infrastructure.

💰 Compensation and Industry Insights:

The base pay for this role ranges from $78,800 to $108,350. This position is also eligible for a Short-Term Incentive (STI) plan.

In the context of the U.S. Data Engineering and Insurance Tech workforce for 2025-2026:

• The “InsurTech” Expansion: In 2026, mid-sized insurance firms like COUNTRY Financial are seeing a 15% increase in data-related hiring as they move toward Predictive Analytics for personalized premium pricing.
• Racial Representation in U.S. Data Science/Dev: Based on 2025 labor data for “Computer and Information Research Scientists/Engineers”:
  • White: 53.2%
  • Asian: 32.5%
  • Hispanic or Latino: 6.8%
  • Black or African American: 5.1%
  • Other/Multiracial: 2.4%
• Gender in Data Engineering: Women currently represent approximately 22% of data engineering and backend developer roles. COUNTRY Financial emphasizes a “small enough to make an impact” culture, which often translates to higher visibility for diverse technical talent.
• PySpark Dominance: Professionals proficient in PySpark for large-scale data manipulation currently command a 12% higher market value compared to those limited to standard Python data libraries.

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