A Data Engineer designs, builds, and maintains scalable data pipelines and infrastructure to enable efficient data collection, storage, and processing. This role is crucial for transforming raw data into reliable, accessible information that drives data-driven decision-making and business intelligence across the organization.
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What is a Data Engineer?
A Data Engineer is a professional responsible for developing, constructing, testing, and maintaining architectures such as databases and large-scale processing systems. They ensure that data flows smoothly from source to destination, making it available for data scientists, analysts, and business users. Data Engineers work with various technologies to handle big data, optimize data retrieval, and support the overall data ecosystem within a company.
What does a Data Engineer do?
Data Engineers design and implement data pipelines to extract, transform, and load (ETL) data from diverse sources into data warehouses or lakes. They develop and maintain databases, optimize data storage solutions, and ensure data quality and integrity. Additionally, they collaborate with data scientists and analysts to understand data requirements, automate data processes, and build infrastructure for advanced analytics and machine learning applications.
Job Overview
We are seeking a skilled Data Engineer to design, build, and maintain scalable data pipelines and infrastructure. This role will be responsible for developing efficient ETL processes, ensuring data quality and reliability, and enabling data-driven decision making across the organization. The ideal candidate will have strong technical expertise in data modeling, cloud platforms, and big data technologies.
Data Engineer responsibilities include:
1. Design and implement scalable data pipelines and ETL processes using Python, SQL, and Spark
2. Develop and maintain data warehousing solutions (Snowflake, BigQuery, or Redshift)
3. Build and optimize data models for efficient storage and retrieval
4. Implement data quality checks and monitoring systems
5. Collaborate with data scientists and analysts to support ML model deployment
6. Manage cloud data infrastructure on AWS, GCP, or Azure platforms
7. Ensure compliance with data governance and security policies
8. Performance tuning and optimization of data processing systems
1. 3+ years of professional data engineering experience
2. Strong proficiency in SQL and Python programming
3. Experience with big data technologies (Spark, Hadoop, or similar)
4. Hands-on experience with cloud data platforms (AWS, GCP, or Azure)
5. Deep understanding of data modeling and database design principles
6. Experience building and maintaining ETL/ELT pipelines
7. Bachelor's degree in Computer Science, Engineering, or related field
Preferred Qualifications
1. Experience with real-time data processing (Kafka, Spark Streaming)
2. Knowledge of containerization technologies (Docker, Kubernetes)
3. Familiarity with data orchestration tools (Airflow, Prefect, or Dagster)
4. Experience with infrastructure-as-code (Terraform, CloudFormation)
5. Previous work in agile development environments
6. Master's degree in a technical field
7. Experience with both batch and streaming data architectures
Bonus Skills
1. Certifications in cloud platforms (AWS Certified Data Analytics, Google Cloud Professional Data Engineer)
2. Experience with machine learning operations (MLOps) pipelines
3. Knowledge of data governance frameworks and tools
4. Contributions to open-source data projects
5. Experience with performance optimization at petabyte scale
6. Background in implementing data mesh or data fabric architectures
7. Previous success in reducing data processing costs by 20%+ through optimization
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