Data Engineer

Position Summary

The Data Engineer is responsible for designing, building, and optimizing scalable data pipelines, data integration solutions, and cloud-based data platforms that support enterprise analytics, reporting, and AI/ML initiatives.

This role serves as a technical expert in data engineering, ensuring reliable data ingestion, high-quality data assets, and efficient data processing while collaborating closely with architects, analysts, and business stakeholders.

Key Responsibilities

Data Engineering & Development

• Design, develop, and maintain scalable and high-performance data pipelines.
• Build and optimize ETL/ELT processes to ingest, transform, and deliver data across enterprise platforms.
• Develop and maintain reusable data engineering frameworks and components.
• Implement data integration solutions for structured and unstructured data sources.
• Support modern cloud-based data platform initiatives.
• Data Ingestion & Pipeline Operations Develop and manage batch and real-time data ingestion pipelines.
• Monitor and optimize pipeline performance, reliability, and scalability.
• Troubleshoot data processing issues and implement corrective actions.
• Automate data workflows and orchestration processes.
• Ensure timely and accurate data delivery to downstream consumers.

Data Quality & Governance

• Implement data validation, reconciliation, and quality controls.
• Support data governance standards, metadata management, and data lineage initiatives.
• Identify and resolve data quality issues and causes.
• Contribute to data observability and monitoring solutions.
• Ensure compliance with organizational data management standards.
• Meridian Platform Support & Implementation Support the implementation and enhancement of Meridian data solutions.
• Develop integrations and data pipelines supporting Meridian initiatives.
• Collaborate with stakeholders to translate business requirements into technical solutions.
• Ensure successful deployment, testing, and operational support of Meridian-related capabilities.

Technical Leadership & Collaboration

• Provide technical guidance and mentorship to junior and mid-level engineers.
• Participate in architecture reviews, design sessions, and code reviews.
• Collaborate with data architects, product owners, analysts, and business stakeholders.
• Promote engineering best practices, automation, and continuous improvement.
• Document technical designs, standards, and operational procedures.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
• 5+ years of experience in Data Engineering or related disciplines.
• Strong experience designing and developing enterprise data pipelines and integration solutions.
• Expertise in SQL and one or more programming languages such as Python, Scala, or Java.
• Experience with ETL/ELT frameworks, data warehousing, and data lake architectures.
• Experience with cloud platforms such as Azure, AWS, or Google Cloud. Knowledge of workflow orchestration tools such as Airflow, Azure Data Factory (ADF), or similar platforms.
• Strong understanding of data quality, data governance, and monitoring concepts.
• Experience troubleshooting and optimizing large-scale data processing solutions.

Requirements

• Experience with Meridian platform implementation or support.
• Experience with Data bricks, Snowflake, Azure Synapse, Microsoft Fabric, or similar modern data platforms.
• Knowledge of streaming technologies such as Kafka, Spark Streaming, or Event Hubs.
• Familiarity with DevOps practices, CI/CD pipelines, Infrastructure as Code (IaC), and automated testing.
• Experience supporting AI/ML data pipelines and feature engineering.
• Relevant cloud, data engineering, or platform certifications.
• Success Measures Reliable and scalable data pipelines with minimal operational issues.
• Improved data quality and reduced production defects. Timely delivery of data engineering solutions and enhancements.
• Successful implementation and support of Meridian data initiatives.
• Increased automation, operational efficiency, and platform stability.
• High-quality technical documentation and adherence to engineering standards.