Senior Data Engineer

Novatalent; United States; On-site; USD 110,000 - 160,000; Full time

Job summary

Novatalent is seeking a Data Engineer to advance our data infrastructure in the United States. The role focuses primarily on maintaining and enhancing our data warehouse and ETL pipelines (about 80%), while contributing to data analysis and reporting (about 20%).

You will partner with cross-functional teams to build robust data solutions and deliver actionable insights through visualizations. The ideal candidate has 3+ years of data engineering experience, strong Python/SQL skills, and hands-on

Qualifications

• 3+ years of experience in data engineering or closely related roles.
• Fluent English communication for collaboration with a U.S.-based team.
• Experience building production data pipelines and data integration workflows.
• Experience with PowerBI or Tableau is a plus.

Responsibilities

• Maintain, enhance, and optimize existing data warehouse architecture and ETL pipelines.
• Design and implement scalable ETL/ELT processes prioritizing data quality and timeliness.
• Monitor pipeline performance, troubleshoot issues, and implement best practices.
• Create and maintain documentation for data engineering processes and configurations.
• Develop PowerBI dashboards and reports to drive business decisions.
• Collaborate with business stakeholders to translate requirements into technical solutions.

Skills

Python

SQL

PySpark

Data modeling

PowerBI

English communication

Tools

Snowflake

BigQuery

Databricks

Airflow

Git

CI/CD

Job description

Job Overview

We are seeking a skilled Data Engineer to join our team and drive our data infrastructure forward. In this role, you will primarily focus on maintaining and enhancing our data warehouse and pipelines (80%) while also contributing to data analysis and reporting initiatives (20%). You’ll work closely with cross‑functional stakeholders to build robust data solutions and create actionable insights through compelling visualizations.

Key Responsibilities

Data Engineering

• Infrastructure Management: Maintain, enhance, and optimize existing data warehouse architecture and ETL pipelines.
• Pipeline Development: Design and implement scalable ETL/ELT processes ensuring data quality, integrity, and timeliness.
• Performance Optimization: Monitor and improve pipeline performance, troubleshoot issues, and implement best practices.
• Documentation: Create and maintain comprehensive documentation for data engineering processes, architecture, and configurations.

Data Analysis & Reporting

• Stakeholder Collaboration: Partner with business teams to gather requirements and translate them into technical solutions.
• Report Development: Build and maintain PowerBI dashboards and reports that drive business decisions.
• Data Modeling: Develop new data models and enhance existing ones to support advanced analytics.
• Insight Communication: Transform complex data findings into clear, actionable insights for various departments.

Required Qualifications

Technical Skills

• Programming & Query Languages: Strong proficiency in Python, SQL, and PySpark.
• Big Data Platforms: Experience with cloud data platforms including Snowflake, BigQuery, and Databricks. Databricks experience highly preferred.
• Orchestration Tools: Proven experience with workflow orchestration tools (Airflow preferred).
• Cloud Platforms: Experience with AWS (preferred), Azure, or Google Cloud Platform.
• Data Visualization: Proficiency in PowerBI (preferred) or Tableau.
• Database Systems: Familiarity with relational database management systems (RDBMS).

Development Practices

• Version Control: Proficient with Git for code management and collaboration.
• CI/CD: Hands‑on experience implementing and maintaining continuous integration/deployment pipelines.
• Documentation: Strong ability to create clear technical documentation.

Experience & Communication

• Professional Experience: 3+ years in data engineering or closely related roles.
• Language Requirements: Fluent English communication skills for effective collaboration with U.S. based team members.
• Pipeline Expertise: Demonstrated experience building and maintaining production data pipelinesk