Data Engineer/Implementation Specialist

Description

Role Overview

Key Responsibilities

• Design, build, and implement data pipelines and ETL/ELT workflows.
• Work closely with business and technical stakeholders to implement data solutions aligned with requirements.
• Develop, optimize, and maintain data models and transformations.
• Integrate data from multiple source systems into centralized data platforms.
• Ensure data quality, reliability, scalability, and performance of pipelines.
• Support deployment, monitoring, troubleshooting, and issue resolution in production environments.
• Collaborate with analytics, application development, and DevOps teams to enable end-to-end data solutions.
• Document data implementations, configurations, and workflows.
• Provide post-go-live support, enhancements, and optimization as needed.
  

Required Skills & Experience

• 3- 6 years of experience as a Data Engineer or Implementation Specialist.
• Strong hands-on expertise in SQL and data modeling concepts.
• Experience working with ETL/ELT tools or data processing frameworks.
• Working knowledge of Python and/or Apache Spark (preferred).
• Solid understanding of data warehousing concepts and dimensional modeling.
• Familiarity with cloud data platforms such as AWS, Azure, or GCP.
• Understanding of data integration, validation, and reconciliation techniques.
  

Nice To Have

• Experience with modern cloud data warehouses such as Snowflake, Redshift, or BigQuery.
• Exposure to workflow orchestration tools like Apache Airflow.
• Knowledge of CI/CD practices for data pipelines.
• Experience in customer-facing or implementation-focused roles.
  

Education

• Bachelors or Masters degree in Computer Science, Engineering, Information Technology, or a related field.
  

Why Join Us

• Opportunity to work on real-world data implementations with measurable business impact.
• Hands-on exposure to modern data engineering tools and cloud platforms.
• Collaborative environment with analytics, engineering, and DevOps teams.
• Competitive compensation and strong growth opportunities in the data engineering domain.