Senior Data Engineer
5 days ago
Job Summary:
As a Senior Data Engineer, you will be responsible for designing, building, and managing complex data architectures, data pipelines, and data warehouses. You will play a critical role in transforming raw data into actionable insights for analytics and business intelligence. You will work with cross-functional teams to design data models, integrate diverse data sources, and optimize data processing workflows.
Key Responsibilities:
- Design and Development:
- Build, implement, and optimize data pipelines for high-performance, scalable data processing.
- Develop data models, schemas, and structures that support both operational and analytical needs.
- Design and implement ETL (Extract, Transform, Load) processes to integrate and clean data from various sources.
- Create efficient data storage solutions using cloud platforms (AWS, Azure, GCP) or on-premise technologies (e.g., Hadoop, Spark).
- Data Infrastructure Management:
- Ensure that data architectures and systems are highly available, secure, and optimized for performance.
- Oversee the setup, configuration, and management of databases, data lakes, and data warehouses (e.g., Snowflake, Redshift, BigQuery, or SQL Server).
- Maintain and manage cloud-based data environments and ensure their scalability and security.
- Collaboration with Stakeholders:
- Work closely with data scientists, business analysts, and stakeholders to understand data requirements and provide efficient data solutions.
- Collaborate with DevOps and software engineers to ensure smooth data integration with other applications and platforms.
- Data Quality and Governance:
- Monitor and ensure data quality, integrity, and consistency across all systems.
- Implement data governance best practices, ensuring compliance with data privacy laws and organizational policies.
- Develop testing frameworks to validate data accuracy and integrity throughout the pipeline.
- Optimization and Performance:
- Tune and optimize SQL queries, data models, and data systems for performance.
- Troubleshoot performance issues and recommend scalable solutions.
- Implement automation and orchestration tools for data workflows to improve efficiency.
- Leadership and Mentoring:
- Provide leadership and guidance to junior data engineers, offering mentorship on best practices and complex data engineering challenges.
- Lead the adoption of new technologies and improvements to the data infrastructure.
- Collaborate with engineering and product teams to ensure data solutions align with business objectives.
Qualifications:
- Education:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, Mathematics, or a related field (or equivalent experience).
- Experience:
- 5+ years of experience as a Data Engineer or in a related role.
- Proven experience designing and building large-scale data processing systems and data pipelines.
- Strong experience with SQL, data modeling, and performance optimization.
- Expertise with big data technologies such as Hadoop, Spark, or Kafka.
- Familiarity with cloud platforms (AWS, Azure, GCP) and related tools for data storage, transformation, and orchestration.
- Technical Skills:
- Proficiency in programming languages such as Python, Java, or Scala for data processing.
- Experience with data storage technologies (e.g., relational databases, NoSQL, columnar databases).
- Expertise in data warehousing solutions like Snowflake, Redshift, or BigQuery.
- Familiarity with orchestration tools such as Apache Airflow, Dagster, or Prefect.
- Experience with version control systems like Git.
- Soft Skills:
- Strong problem-solving skills and the ability to work independently on complex technical challenges.
- Excellent communication and collaboration skills to work across teams.
- Ability to explain complex technical concepts to non-technical stakeholders.
Preferred Skills:
- Experience with machine learning model deployment and working with data science teams.
- Knowledge of data security practices and privacy regulations.
- Familiarity with containerization technologies such as Docker and Kubernetes.
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