About Alpha Theory: Alpha Theory is a fintech software company that prides itself on an inclusive and transparent culture that attracts curious teammates who are excited about tackling big ideas. We hire smart, motivated team members that crave feedback to be better. We believe that good ideas come from all ranks of the team as we all strive to create the best possible version of Alpha Theory.
Key Responsibilities:
Building and maintaining robust data pipelines that ingest, process, and deliver data to data science teams and other stakeholders
Ensuring data pipelines are scalable, reliable, and meet performance requirements
Collaborating with data scientists and analysts to understand data requirements and ensure data is accessible for analysis
Designing, developing, and maintaining ETL processes to transform and load data into analytical data stores
Optimizing and automating data extraction, transformation, and loading tasks
Designing and developing modern automated tooling and scripts for data validation and monitoring
Monitoring data for anomalies, errors, and inconsistencies and taking corrective actions as needed
Assisting in migration of current data science SQL infrastructure to more modern and scalable infrastructure (preferably in the cloud and postrgres)
Assisting in managing and administering Alpha Theory's SQL Server instances
Requirements Required Qualifications:
2-3 years of experience as a data engineer or software engineering experience (including data engineering)
Bachelor's or higher degree in Computer Science, Data Science, or a related field (or comparable experience)
Expertise in Python, SQL Server, and Git (GitHub)
Experience in ETL processes and data pipeline development & management
Experience in data quality assurance, including data cleansing and validation
Knowledge of database systems, data warehousing, and data modeling
Excellent problem-solving and troubleshooting skills
Strong communication and teamwork abilities
Preferred Qualifications:
Preferred experience with tools and technologies commonly used in data engineering (e.g., Apache Spark, Airflow, Docker, AWS, etc.)
Experience with financial data
Familiarity with data science and analytics concepts