Data Engineer

Key Responsibilities:

Data Architecture Design:

• Design scalable and efficient data architectures that support the organization's digital platforms and applications.
• Develop logical and physical data models that represent business processes and data flows.
• Define data storage solutions, including relational databases, data lakes, data warehouses, and NoSQL databases, based on use cases and requirements.

Data Integration and Management:

• Lead the integration of data from various sources, ensuring consistency, accuracy, and timeliness.
• Implement data pipelines and ETL (Extract, Transform, Load) processes to facilitate data movement and transformation.
• Manage and optimize the performance of databases and data storage systems, ensuring low latency and high availability.

Data Governance and Security:

• Establish data governance frameworks, including data quality standards, data lineage, and metadata management.
• Define and enforce data security policies, including data encryption, access controls, and compliance with relevant regulations (e.g., GDPR, HIPAA).
• Ensure data privacy and protection measures are in place, particularly for sensitive and personally identifiable information (PII).

Collaboration and Communication:

• Work closely with business stakeholders, data scientists, data engineers, and software developers to understand data requirements and translate them into architectural solutions.
• Provide guidance on data-related issues and best practices, ensuring alignment between data architecture and business objectives.
• Document data architecture designs, data models, and processes for knowledge sharing and future reference.

Innovation and Continuous Improvement:

• Stay informed about the latest data technologies, tools, and industry trends to drive innovation in data architecture.
• Evaluate and recommend new data management technologies and platforms to improve the organization's data capabilities.
• Lead efforts to improve data architecture, including refactoring, optimization, and migration of legacy systems to modern platforms.

Data Analytics and Reporting:

• Support the development of data analytics platforms by ensuring that data architectures are designed for efficient querying and reporting.
• Collaborate with data analysts and business intelligence teams to develop dashboards, reports, and data visualizations.
• Implement data cataloging and discovery tools to enable self-service data access for business users.

Required Qualifications:

• Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field.
• Extensive experience in data architecture, data modeling, and database design.
• Proficiency with data management tools and technologies, including relational databases (e.g., SQL Server, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), and cloud data platforms (e.g., AWS Redshift, Azure Synapse).
• Strong understanding of data integration, ETL processes, and data warehousing concepts.
• Knowledge of data governance practices, data security standards, and regulatory compliance requirements.
• Experience with big data technologies, such as Hadoop, Spark, or Kafka.
• Familiarity with data analytics tools, such as Tableau, Power BI, or Looker.

Preferred Qualifications:

• Advanced degree (Master's or PhD) in a relevant field.
• Certifications in data management or cloud platforms (e.g., AWS Certified Big Data Specialty, Google Professional Data Engineer).
• Experience with machine learning and AI-driven data architectures.
• Knowledge of microservices and API-driven data integration.
• Proficiency in programming languages such as Python, Java, or Scala.