Data Engineer

Overview

We are looking for a skilled and driven Data Engineer to architect, develop, and oversee scalable and efficient data platforms and pipelines. The successful applicant will have extensive experience managing large-scale data environments and modern cloud data tools, emphasizing data quality, validation, cleansing, and governance.

This position demands a proactive professional capable of independent work, collaborating with clients and stakeholders, and contributing strategically. We seek a technology-agnostic individual who adapts easily to evolving technical landscapes and is committed to enhancing processes, standards, and workflows continuously.

Key Responsibilities

• Create, implement, and sustain scalable data pipelines along with ETL/ELT frameworks.
• Develop and improve data solutions to handle large sets of both structured and unstructured data.
• Design future-ready data architectures supporting analytics, reporting, machine learning, and BI initiatives.
• Ensure data systems are secure, reliable, scalable, and easy to maintain.
• Support both real-time and batch data processing requirements.
• Develop, uphold, and monitor data validation, quality, and cleansing processes.
• Set up monitoring, auditing, and alerting systems to maintain data integrity.
• Detect and resolve anomalies, inconsistencies, and performance issues within data.
• Implement best practices for data governance, lineage, and metadata management.
• Utilize platforms and tools such as Databricks, Snowflake, Azure Synapse Analytics, SQL Server, PostgreSQL, and cloud/hybrid data environments.
• Optimize database efficiency, query performance, and data storage methods.
• Assist in data migration, modernization, and system integration projects.

Required Skills and Qualifications

• Minimum 4 years' experience designing, constructing, and managing data platforms and solutions.
• Bachelor’s degree in Computer Science, IT, Engineering, or related field.
• Proficiency with Databricks, Snowflake, Azure Synapse Analytics, Microsoft SQL Server, PostgreSQL, and SQL development/optimization.
• Hands-on experience creating ETL/ELT pipelines and data integration from multiple sources.
• Solid understanding of data modeling, warehousing, and lakehouse architecture principles.
• Experience with implementing data quality controls, validation, cleansing, and reconciliation processes.
• Knowledge of cloud-native data platforms and contemporary data engineering best practices.
• Familiarity with CI/CD, DevOps, automation, and infrastructure as code is advantageous.
• Self-driven, capable of independent work, and effective in client-facing environments.
• Adaptability across diverse technologies and readiness for dynamic changes.
• Positive, teamwork-oriented, and solution-driven mindset.