Data Engineer

Vital IT Services; Allen (TX); On-site; USD 90,000 - 130,000; Full time

Job summary

Vital IT Services is looking for a Data Engineer to design, build, and maintain highly reliable data pipelines, storage systems, and schemas for SaaS products and client reporting. You will optimize data flows, ensure security and SOC 2 compliance, and collaborate with cross-functional teams on data architectures.

The role requires 3–5 years of experience with SQL, Python, and modern cloud data tools and a strong track record in high-compliance environments. WFO in Allen, TX may apply.

Qualifications

• 3–5 years of dedicated experience as a Data Engineer or Database Developer
• Advanced SQL including complex joins, queries, and optimizations
• Proficiency in Python or similar scripting languages for data extraction and cleaning
• Experience with cloud data services and data pipeline engines (Azure Data Factory, Airflow, dbt)
• Understanding of star/snowflake schemas and relational modeling

Responsibilities

• Design, construct, and maintain automated data pipelines (ETL/ELT) to ingest, clean, and transform data sources
• Model logical and physical database schemas across relational and non-relational platforms
• Build and optimize centralized data warehouses and staging areas for reporting
• Tune query performance, indexing, and storage strategies to meet SLAs
• Implement data security controls and SOC 2/compliance measures

Skills

Advanced SQL

Python scripting

Query optimization

Cloud data services

Data warehousing concepts

Tools

Azure SQL

Cosmos DB

AWS Athena

Snowflake

Azure Data Factory

dbt

Airflow

Job description

Core Focus

To design, build, and maintain highly reliable data pipelines, storage systems, and transformational schemas; ensuring that secure, clean, and optimized data flows continuously across all core SaaS products and client reporting systems.

Roles & Responsibilities

1. ETL & Data Pipeline Engineering: Designing, constructing, and maintaining automated data pipelines (ETL/ELT) to ingest, clean, and transform disparate data sources into organized, high-performance repositories.
2. Database & Schema Modeling: Designing logical and physical database schemas across relational (e.g., SQL Server, Azure SQL) and non-relational (e.g., Cosmos DB) platforms to support application scale and fast query execution.
3. Data Warehousing & Architecture: Building and optimizing centralized data warehouses or staging environments that aggregate complex transactional insurance data for downstream reporting systems.
4. Query Performance Tuning & Optimization: Monitoring, profiling, and tuning database performance - including query execution plan analysis, index optimization, and storage strategy adjustments.
5. Data Security & Compliance Blueprinting: Implementing rigorous access controls, data masking, encryption standards, and retention policies to ensure absolute compliance with SOC 2 and insurance data-privacy rules.

Skills & Experience

• Professional Core Experience: 3-5 years of dedicated experience operating as a Data Engineer or Database Developer managing complex, multi-source data environments (experience in high-compliance SaaS or financial services is a major plus).
• Advanced SQL & Procedural Scripting: Expert-level mastery of advanced SQL (including writing highly performant queries, complex joins, subqueries, and database optimization techniques).
• Data Pipe Programming: Solid experience writing scripts in Python or similar development languages to run automated API data extractions, cleansing routines, and custom integrations.
• Modern Cloud Infrastructure: Strong practical experience with cloud-native data services (e.g., Azure SQL, Cosmos DB, AWS Athena, or Snowflake) and cloud data pipeline engines (e.g., Azure Data Factory, dbt, or Airflow).
• Relational and Dimensional Modeling: Deep conceptual understanding of star schemas, snowflake schemas, and relational database normalization vs. denormalization strategies.

Success Metrics

• Data Pipeline Uptime & Delivery: Maintain a greater than or equal to 99% success rate on scheduled ETL pipeline executions and automated data transfers.
• Query Response Time Baseline: Ensure key production databases maintain a target average query latency under 200ms for standard transactional read operations.
• Data Delivery Integrity Rate: Zero critical production incidents caused by data corruption, schema mismatches, or missing automated load steps per quarter.
• Support & Reporting Team Unblocked SLA: Resolve internally flagged database or schema pipeline blockages in an average of less than 4 hours to keep downstream business intelligence teams moving.