We are a fast-growing, nationwide personal injury law firm seeking an experienced, high-impact Data Engineer to design and build a world-class data infrastructure. Our firm operates on Salesforce, AWS, Amazon Connect, and various other platforms, generating vast amounts of valuable data. We're looking for a Data Engineer with at least 5 years of experience who can architect, optimize, and scale our data ecosystem - so our analysts and data scientists can focus on insights instead of wrangling data. Responsibilities: • Build & optimize ETL/ELT pipelines using BigQuery, AWS (S3, Redshift, Glue, Lambda), and Salesforce data • Design and manage scalable data warehouses & lakes that support real-time and batch processing • Work closely with analysts and stakeholders to ensure clean, structured, and accessible data • Implement best practices in data governance, security, and performance optimization • Automate data workflows to minimize manual effort and enhance data reliability • Develop API integrations to centralize data from CRM, marketing, finance, and telephony systems Qualifications: • 5+ years of data engineering experience in a fast-paced environment • Bachelor’s degree in Information Systems, Analytics, Mathematics, Computer Science, Engineering, or related field required • Expertise in BigQuery, AWS (S3, Redshift, Glue, Lambda), and SQL-based data pipelines • Strong experience working with Salesforce data architecture • Proficiency in Python and SQL for data transformation and automation • Ability to roadmap, design, and build from scratch - not just maintain existing systems • Experience working with BI tools (e.g., Looker Studio, Tableau, Power BI) is a plus Compensation: $110,000 - $130,000 yearly
• Build & optimize ETL/ELT pipelines using BigQuery, AWS (S3, Redshift, Glue, Lambda), and Salesforce data • Design and manage scalable data warehouses & lakes that support real-time and batch processing • Work closely with analysts and stakeholders to ensure clean, structured, and accessible data • Implement best practices in data governance, security, and performance optimization • Automate data workflows to minimize manual effort and enhance data reliability • Develop API integrations to centralize data from CRM, marketing, finance, and telephony systems