Job Description
Hybrid Details: Initially on-site 5 days/week in Dallas, TX, then hybrid (2 days/week) eventually Job Description The organization is seeking a� Senior � Data Engineer �to work on our growing Corporate Reporting and Analytics team.�This role is ideal for a technically versatile problem-solver who can work end-to-end across data engineering, automation/orchestration, cloud infrastructure, and front-end analytics application development. Beyond technical excellence, this individual will play a key role in driving business impact helping teams make smarter decisions, identifying inefficiencies, and introducing practical automation that improves daily workflows across the company. Ideal Candidate Profile You thrive in environments where versatility and problem-solving matter as much as specialization. You understand both the technical and business sides of a challenge, and you take initiative in identifying opportunities to improve workflows, automate manual tasks, and deliver tools that make teams more efficient. You enjoy taking ownership of a problem from start to finish understanding the business context, analyzing the data, shaping it, automating it, deploying the solution, and presenting it through intuitive tools. You are excited about the future of AI-assisted development and eager to apply it to accelerate impact within the Analytics team. Who You Are Bachelor's degree in computer science, Business Information Systems or similar. Approximately 5+ years of experience in analytics engineering, data engineering, or a closely related technical field Strong problem-solving abilities with a passion for learning and quickly mastering new technologies Comfortable working across multiple layers of the stack (data pipelines ? cloud ? orchestration ? application layer). Not just a front end Javascript Developer or just a backend DB or ETL Developer. Able to translate technical capabilities into meaningful business outcomes Strong communication and collaboration skills Curious about AI-assisted development tools and able to incorporate them into your workflows Motivated by uncovering operational inefficiencies and proposing practical, technology-driven improvements Technology Environment Will Work In Data Engineering & Analytics Strong SQL skills (MySQL, PostgreSQL, DuckDB, or similar) Experience in data modeling, warehousing concepts, and data transformation Experience building and maintaining end-to-end pipelines Programming & Application Development Proficiency in Python Experience with front-end analytics tools and frameworks (Plotly/Dash preferred; Tableau, Power BI, Looker, or similar acceptable) Familiarity with JavaScript or willingness to learn Cloud & Infrastructure Experience with at least one major cloud provider (AWS preferred, though GCP/Azure comparable) Familiarity with cloud services such as compute, storage, networking, and IAM concepts Orchestration & Dev Tools Experience with orchestration tools (Airflow preferred; Dagster, Prefect, etc., accepted) Experience with version control and development workflow tools (Jira, Bitbucket, GitHub, etc.) AI-Assisted Development Familiarity with AI-assisted coding tools (e.g., OpenAI Codex, GitHub Copilot, or equivalents) What You Ll Do Build and maintain scalable, automated data pipelines and architectures supporting stakeholders across the business Develop, deploy, and maintain internal analytics applications and dashboards using Plotly/Dash or equivalent tools Design and orchestrate workflows using Airflow or similar orchestration platforms Work within cloud environments to support application and pipeline deployment (high-level familiarity acceptable; deep specialization not required) Gather, transform, and prepare data for analysis and visualization Develop high-quality, maintainable code aligned with best practices Document systems, processes, and data flows clearly and thoroughly Communicate complex technical topics in ways that non-technical stakeholders can easily understand Contribute to an end-to-end development lifecycle from data ingestion through to user-facing tools Identify opportunities to streamline business processes and introduce technology-driven automation Recommend data-driven improvements that support smarter decision-making across departments