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.