✨ AI Summary
The Senior Data Engineer will be responsible for designing, building, and maintaining robust ETL/ELT pipelines, developing and optimizing data ingestion, transformation, and processing workflows, and ensuring data reliability, performance, and scalability. This role involves implementing data quality, validation, and monitoring within pipelines, and supporting analytics, BI, and AI/ML use cases with trusted datasets. Key technical skills include strong SQL expertise, proficiency in Python, Spark, or Scala, hands-on experience with Data Warehouses, Data Lakes, and Lakehouse architectures, and experience with cloud platforms (AWS, Azure, or GCP). Experience with orchestration tools like Airflow, Azure Data Factory, or Dagster, and streaming/real-time data technologies such as Kafka, is also required. The ideal candidate will have 5+ years of experience in data engineering, data platforms, or analytics engineering, with proven experience building and operating scalable, production-grade data pipelines in complex, multi-source enterprise environments. A Bachelor's degree in computer science, Engineering, or a related field is necessary, and cloud or data engineering certifications are considered a plus. The role acts as a technical lead, mentoring junior engineers and collaborating with product, analytics, and business teams.
Requirements
Requires a Bachelor's degree in computer science, Engineering, or a related field. Must have 5+ years of experience in data engineering, data platforms, or analytics engineering, with proven experience building and operating scalable, production-grade data pipelines in complex enterprise environments. Cloud or data engineering certifications are a plus.
Description
Core Responsibilities: Design, build, and maintain robust ETL/ELT pipelines.Develop and optimize data ingestion, transformation, and processing workflows.Ensure data reliability, performance, and scalability.Implement data quality, validation, and monitoring within pipelines.Support analytics, BI, and AI/ML use cases with trusted datasets.Technical Skills:Strong expertise in SQL and performance tuning.Proficiency in Python, Spark, or Scala.Hands-on experience with Data Warehouses, Data Lakes, Lakehouse architectures.Hand-on experience with Cloud platforms (AWS, Azure, or GCP).Experience with orchestration tools (Airflow, Azure Data Factory, Dagster).Experience with streaming / real-time data (Kafka, event-driven pipelines).Familiarity with CI/CD, Git, and DevOps practices.Data Architecture & Integration:Ability to work closely with Data Architects to implement target architectures.Experience integrating data from APIs, databases, files, and SaaS platforms.Understanding of data modeling and analytics-ready schemas.Data Governance & Security:Awareness of data governance, data quality, and metadata practices.Experience implementing data access controls and security.Understanding of privacy and compliance requirements.Leadership & Collaboration:Acts as a technical lead on data engineering initiatives.Mentors junior data engineers and reviews code.Strong collaboration with product, analytics, and business teams.Experience:5+ years of experience in data engineering, data platforms, or analytics engineering.Proven experience building and operating scalable, production-grade data pipelines.Experience working in complex, multi-source enterprise environments.Education & Certifications:Bachelor's degree in computer science, Engineering, or related field.Cloud or data engineering certifications are a plus.