Senior Data Engineer - Databricks, SQL & ETL Specialist

Acute Talent; United States; On-site; USD 100,000 - 140,000; Full time

Job summary

Acute Talent is seeking a skilled Data Engineer to join our data analytics team. You will work across cross‑functional projects to design, build, and optimize data pipelines, frameworks, and warehouses to support business‑critical analytics and reporting.

The role requires deep experience in SQL-based transformations, Databricks, and modern data engineering practices, plus collaboration with analytics teams to define data models, governance, and scalable data solutions.

Qualifications

• Experience building data pipelines and ETL processes.
• Strong SQL skills for data extraction, transformation, and loading.
• Knowledge of data governance and data quality practices.

Responsibilities

• Design, develop, and maintain datapipelines and ETL processes using Databricks and SQL.
• Write optimized SQL queries for data extraction, transformation, and loading across large-scale datasets.
• Monitor and optimize data movement, cleansing, normalization, and updating to ensure data quality.
• Collaborate with business and analytics teams to define data models, schemas, and frameworks within the data warehouse.
• Document source-to-target mapping and transformation logic.
• Build data frameworks and visualizations to support analytics and reporting.
• Ensure compliance with data governance, security, and regulatory standards.
• Communicate effectively with stakeholders to understand and deliver on data needs.

Skills

Databricks

SQL

Data engineering

Education

Bachelor's degree in Computer Science or related field

Job description

Acute Talent is seeking a skilled Data Engineer to join our data analytics team. You will work across cross‑functional projects to design, build, and optimize data pipelines, frameworks, and warehouses to support business‑critical analytics and reporting.

The role requires deep experience in SQL-based transformations, Databricks, and modern data engineering practices, plus collaboration with analytics teams to define data models, governance, and scalable data solutions.