Data Engineer

Responsibilities:

• Design and implement data pipelines and ETL infrastructure using Airbyte connectors to facilitate efficient and reliable data extraction, ingestion, and integration.
• Utilize DBT to create modular and scalable datasets, enabling advanced analytics and reporting tailored to the marketing automation domain.
• Work with the data science team to develop practical and industry-based standards for data analytics
• Collaborate with cross-functional teams to seamlessly integrate data solutions into Capture's AI-driven marketing automation platform.
• Facilitate smooth data flow between different components of the data infrastructure.
• Design and implement databases based on data modeling principles, prioritizing optimization for performance, reliability, and security.
• Collaborate with database administrators to ensure the integrity and security of financial data storage.
• Implement data quality checks and validation processes within DBT and other tools to maintain high-quality data standards.
• Proactively identify and address data quality issues, ensuring compliance with privacy regulations.
• Document implementation processes, configurations, and best practices.
• Provide training and support to end-users, analysts, and team members.
• Continual learning and self-guided education in industry- and skill-specific programs (data architecture, database management, and analytics).

Requirements:

• Proven experience as a data engineer with a focus on Airbyte and DBT.
• Language proficiencies (SQL, Python, R).
• Hands-on experience with ETL processes and workflow orchestration.
• Proficiency in DBT for data modeling and transformation.
• Experience in customer-facing roles.
• Experience working and collaborating in a diverse and distributed global team environment.
• Experience with creating database schemas and cloud-based big data architecture such as MySQL, Athena/Hive/Hadoop, EMR, Redshift, and Apache Spark.
• Database architecture and maintenance, machine learning, predictive modeling, documenting DAGs.
• Experience with creating data efficiencies and solving optimization problems.

Preferred Skills:

• Certification or advanced degree requiring data science skills (open to discipline or area of expertise).
• Experience working with educational data sets.
• Experience with data visualization tools (e. g., Shiny, AWS QuickSight).
• Familiarity with PHP/Laravel.