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Data Engineer

LinkedIn Resolve Tech Solutions Richardson, TX
Mid-Senior level Posted April 2, 2026 Job link
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Requirements
  • The ideal candidate is a "polyglot" coder who can seamlessly transition between R for statistical analysis and Python for application logic and automation, while maintaining high-performance SQL scripts to power our backend data structures.
  • Data Development: Design, develop, and maintain automated data pipelines and scripts using Python and R to support large-scale data processing.
  • Database Management: Write and optimize complex SQL queries, stored procedures, and views to extract data from relational databases.
  • Advanced Reporting: Build sophisticated automated reporting tools and financial models within MS Excel using advanced formulas, Power Query, or VBA/Macros where necessary.
  • Process Automation: Identify manual data workflows and replace them with efficient, scalable Python or R-based automation scripts.
  • Collaboration: Work closely with cross-functional teams to define data requirements and deliver technical solutions that meet business objectives.
  • Programming: Mastery of Python (Pandas, NumPy, Scikit-learn) and R (tidyverse, ggplot2, Shiny) for data manipulation and visualization.
  • Database: High proficiency in SQL (PostgreSQL, SQL Server, or Oracle) including performance tuning and schema design.
  • Excel Mastery: Advanced knowledge of MS Excel , including Pivot Tables, VLOOKUP/XLOOKUP, Power Pivot, and complex nested logic.
  • Environment: Experience working in a hybrid environment, with the ability to collaborate effectively during in-office days in Richardson.
  • Experience: 3+ years of professional experience in a data-centric role (Data Engineer, Data Scientist, or Senior Data Analyst).
  • Communication: Ability to explain complex technical findings to non-technical stakeholders.
  • Flexibility: Must be able to commute to the Richardson, TX office every Tuesday and Thursday
Preferred Skills
  • The ideal candidate is a "polyglot" coder who can seamlessly transition between R for statistical analysis and Python for application logic and automation, while maintaining high-performance SQL scripts to power our backend data structures.
  • Database: High proficiency in SQL (PostgreSQL, SQL Server, or Oracle) including performance tuning and schema design.
Education
  • (Not required) – Education: Bachelor s degree in Computer Science, Statistics, Mathematics, Data Analytics, or a related quantitative field.