A Senior Data Engineer designs, builds, and maintains scalable and reliable data infrastructure and pipelines, enabling organizations to efficiently collect, store, process, and analyze vast amounts of data for critical business insights and decision-making.
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What is a Senior Data Engineer?
A Senior Data Engineer is an experienced professional responsible for the end-to-end architecture and implementation of data systems. They possess deep expertise in data modeling, ETL/ELT processes, and big data technologies, ensuring that data is accessible, reliable, and optimized for analytical and operational use. This role requires strong programming skills, a solid understanding of distributed systems, and the ability to mentor junior engineers while collaborating with data scientists and analysts to support data-driven initiatives.
What does a Senior Data Engineer do?
Senior Data Engineers design and construct robust data pipelines to ingest, transform, and load data from various sources into data warehouses or data lakes. They optimize database performance, implement data governance and security measures, and automate data workflows to improve efficiency and reliability. Additionally, they troubleshoot data issues, perform system monitoring, and provide technical guidance to teams, ensuring that data infrastructure supports the organization's analytical needs and business objectives.
Job Overview
We are seeking an experienced Senior Data Engineer to design, build, and maintain scalable data pipelines and infrastructure. The ideal candidate will be responsible for developing robust ETL processes, optimizing data workflows, and ensuring data quality and reliability across our organization. This role requires strong technical expertise in big data technologies and cloud platforms, with a focus on delivering data solutions that drive business insights and decision-making.
Senior Data Engineer responsibilities include:
1. Design and implement scalable data pipelines and ETL processes using tools like Apache Spark, Airflow, and Kafka
2. Develop and maintain data models, data warehouses, and data lakes on cloud platforms such as AWS, GCP, or Azure
3. Optimize data processing and storage for performance, cost, and scalability
4. Ensure data quality, integrity, and security through validation, monitoring, and governance practices
5. Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions
6. Troubleshoot and resolve data-related issues in production environments
7. Mentor junior engineers and contribute to best practices in data engineering
1. 5+ years of experience in data engineering or related roles
2. Proficiency in SQL and at least one programming language (Python, Java, or Scala)
3. Hands-on experience with big data technologies (e.g., Hadoop, Spark, Kafka)
4. Strong knowledge of data modeling, ETL development, and data warehousing concepts
5. Experience with cloud data platforms (AWS Redshift, S3, BigQuery, or Azure Data Lake)
6. Familiarity with workflow orchestration tools like Airflow or Luigi
7. Bachelor's degree in Computer Science, Engineering, or a related field
Preferred Qualifications
1. Experience with real-time data processing and streaming technologies
2. Knowledge of machine learning pipelines and MLOps practices
3. Familiarity with containerization and orchestration tools (Docker, Kubernetes)
4. Experience with data governance, security, and compliance frameworks
5. Master's degree in a relevant technical field
6. Certifications in cloud platforms (AWS, GCP, or Azure)
Bonus Skills
1. Contributions to open-source data projects or relevant technical blogs
2. Experience with graph databases or NoSQL databases (e.g., Neo4j, Cassandra)
3. Knowledge of data visualization tools (Tableau, Looker, or Power BI)
4. Experience in optimizing data pipelines for cost and performance in large-scale environments
5. Background in DevOps/DataOps practices and CI/CD pipelines
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