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We’re partnering with a fast-growing organization seeking a experienced Data Engineer to help architect and optimize data pipelines and infrastructure across complex enterprise systems. This role is ideal for someone who thrives in dynamic environments, has deep experience with cloud platforms, and enjoys building scalable, production-ready solutions that support analytics, data science, and business intelligence.
Job Responsibility:
Design, build, and maintain robust, scalable ETL/ELT pipelines and data models
Partner with analytics, product, and engineering teams to translate business needs into technical solutions
Optimize data processing workflows for speed, reliability, and cost-efficiency
Implement data quality controls, monitoring, and observability across data flows
Work with structured and unstructured data across cloud-based data platforms (AWS, Azure, or GCP)
Maintain and improve data warehouse environments (e.g., Snowflake, Redshift, BigQuery)
Apply best practices for data governance, security, and compliance
Requirements:
5–10 years of experience in a Data Engineering or similar technical role
Strong programming skills in Python or Scala, with SQL proficiency
Hands-on experience with modern data tools such as Airflow, dbt, Spark, Kafka, or similar
Experience with cloud data infrastructure (AWS preferred
Azure/GCP also acceptable)
Solid understanding of data architecture principles and data warehousing concepts
Familiarity with CI/CD practices and version control (Git, Jenkins, etc.)
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
Nice to have:
Experience supporting data science, ML pipelines, or real-time data systems
Familiarity with tools like Fivetran, Talend, or Informatica
Exposure to containerization (Docker, Kubernetes) and DevOps practices
What we offer:
Competitive compensation and benefits package
Opportunities to influence architectural direction and tool selection
Medical, vision, dental, and life and disability insurance
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