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Management / Experienced Remote Python Data Jobs
Data Analyst 2
Science, Statistics, Applied Math, Economics or related field (Master's or Doctoral preferred) 3+ years Company experience in data analytics or a combination Company experience and advanced degree Proficiency in statistical programming languages such as R, Python..., and SQL Experience with data visualization tools like Power BI and Tableau Knowledge Company machine learning and AI-based productivity tools
Senior Machine Learning Engineer
support Required Qualifications 5+ years of experience in machine learning engineering, data infrastructure, or platform engineering Proven track record of leading multi-stakeholder projects in scalable ML tooling or training systems Proficiency in Python...and experience with distributed data processing environments Hands-on experience with large-scale data pipelines and cloud data storage solutions Curiosity and adaptability to master new ML and data technologies
Data Analyst
outcomes for accurate forecasting Required Qualifications 5+ years of experience in data analysis, business intelligence, or financial analysis, or a relevant Bachelor's degree with 3+ years of experience 5+ years of experience with SQL, KQL, or Python...for data querying and transformation 5+ years of experience developing and maintaining ETL pipelines 5+ years of experience with data visualization tools and data storytelling best practices
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Engineering Manager
Key Responsibilities Lead, coach, and develop a team of software and data engineers Own the architecture for protocol indexers and data pipelines in Python and Go Drive team planning and prioritization across maintenance and new protocol coverage...with data modeling and data quality frameworks Strong collaboration skills with cross-functional teams
Data Engineer
Engineers to translate data requirements into engineering solutions Required Qualifications 3+ years of experience as a Data Engineer focused on data pipeline development and data warehousing Strong hands-on experience with data modeling and cloud data...warehouses Proficiency in Python and SQL for data manipulation and analysis Experience with data ingestion tools and ETL/ELT processes Experience with Airflow
Senior Data Engineer
running over 10,000 data pipelines daily Design and implement a Data Quality framework and user-facing services for data ingestion Required Qualifications At least 4 years of experience in data engineering Strong programming skills, preferably in Python...Expertise in big data engineering and web services Experience with cloud platforms, ideally AWS Willingness to build diverse components and drive platform evolution
Lead QA Automation Engineer
Key Responsibilities Perform hands-on data engineering and automated testing for data pipelines Utilize SQL, Python, and Pandas libraries for data extraction, transformation, and loading Test for load, soak, and resilience in a remote enterprise environment
Data Engineer
cross-functional teams to deliver solutions aligned with business objectives and data governance Required Qualifications 3+ years of experience in modeling, building, and maintaining data solutions in cloud-based environments Proficiency in SQL and Python...for data manipulation and management Bachelor's degree in Computer Science, MIS, or CIS, or equivalent experience 8+ years of experience for senior level candidates, with proven impact in BigQuery and cloud-based data warehousing Advanced knowledge
Senior Software Engineer
Key Responsibilities Design and build robust backend systems using Python for data platform engineering Develop and maintain data pipelines using Kafka and other streaming technologies Build and optimize APIs for integration between internal teams
Program Manager in Data Science
Data Science, Statistics, Mathematics, or a related field Three years of experience in data science or quantitative analysis Experience with statistical modeling, machine learning, and deep learning techniques Proficiency in SQL and programming in Python...for data analysis One year of experience in designing and deploying model pipelines into production environments
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