Data Engineer Lead

TalentOla; Charlotte (NC); On-site; USD 63,000 - 135,000; Full time

Job summary

TalentOla is seeking a Lead Data Engineer in Charlotte to lead design, development, and maintenance of scalable data pipelines and ETL processes. You will guide a team, work with AWS Glue, PySpark, and Apache Iceberg, and apply Generative AI to automate data analysis and documentation.

The role requires 12+ years in data engineering, a BS in a related field, and strong communication with senior leadership. Insurance data domain experience is a plus.

Qualifications

• At least 12+ years in data engineering with Lead roles.
• Bachelor’s degree in computer science, IT, engineering, or related field.
• Education from a reputed college.

Responsibilities

• Translate business needs into S2T data mappings.
• Design and develop robust data pipelines for scalable ETL.
• Create synthetic datasets for testing across source systems.
• Develop automated data validation tools with Python.
• Ensure best practices in project execution and testing.
• Leverage generative AI to enhance data analysis and documentation.
• Collaborate with leaders to improve processes with data-driven insights.

Skills

Data pipelines

ETL development

Governing data platforms

AWS data engineering

Mentorship

Communication with leadership

Generative AI for data

Education

Bachelor's degree in CS/IT/Engineering

Education qualification: Any degree from a reputed college

Tools

AWS Glue

PySpark

Apache Iceberg

IAM/S3/Secrets Manager

MySQL/Db2/PostgreSQL/Snowflake

Job description

Role: Lead Data Engineer -- Location:(Charlotte) Salary range $63000 to $134500

We're seeking an experienced pipeline-centric data engineer to put it to good use in building out ETL and Data Operations framework (Data Preparation / Normalization and Ontological processes).

Technical Skills:

• Lead the design, development, and maintenance of scalable data pipelines and ETL processes, ensuring data integrity and accessibility for business intelligence and advanced analytics.
• Architect and manage robust data platforms on the AWS ecosystem, leveraging services like Glue, PySpark, Apache Iceberg, IAM, S3, and Secrets Manager to build a secure and efficient data infrastructure.
• Provide technical guidance and mentorship to a team of data engineers, fostering a culture of high performance and continuous learning.
• Utilize deep expertise in various RDBMS (e.g., MySQL, Db2, PostgreSQL, Snowflake) and different data formats (e.g., JSON, Parquet) to drive strategic data initiatives.
• Champion the adoption of modern data technologies such as Apache Iceberg with AWS Glue to optimize data lake performance and analytics capabilities.
• Apply advanced proficiency in Generative AI to automate and streamline data analysis, development, and documentation processes.
• Business Acumen and Stakeholder Communication
• Translate complex business challenges into clear, data-driven solutions, effectively communicating technical concepts and project progress to both technical and non-technical stakeholders, including senior leadership.
• Act as a key liaison between the data engineering team and business units, providing data-backed insights and recommendations that directly influence business strategy.
• Manage concurrent projects in a dynamic, research-oriented environment, ensuring timely delivery and high-quality outcomes.
• Experience in insurance domain preferrable
• AWS Data Engineering certification good to have

Key Responsibilities

• Collaborate with business analysts and stakeholders to translate business needs into comprehensive source-to-target (S2T) data mappings.
• Lead the design and development of robust data pipelines, using your deep understanding of existing ETL frameworks to build scalable and efficient solutions.
• Analyze and understand diverse source systems and data formats to create and validate synthetic datasets for testing and development.
• Develop automated data validation tools using Python to ensure data integrity across various ETL layers.
• Ensure Industry best practice is followed in all aspects of project.
• Develop robust development testing approach to minimize the quality, integration and user testing issues.
• Leverage generative AI to enhance data analysis, accelerate development, Unit Testing and streamline documentation.
• Apply statistical analysis to large datasets, uncovering key patterns, identifying potential challenges, and generating actionable insights.
• Partner with business leaders to understand their challenges and provide data-driven recommendations that improve processes and inform strategic decisions.

Qualification:

• Somebody who has at least 12+ years of data engineering experience has played Lead Data Engineer role.
• Bachelor's degree (or equivalent) in computer science, information technology, engineering, or related discipline
• Education qualification: Any degree from a reputed college