Apply

Data Engineering Manager

Posted 2 days agoViewed

View full description

💎 Seniority level: Manager, 7+ years

🔍 Industry: AI solutions for the Group Health and P&C insurance industries

🏢 Company: Gradient AI👥 101-250💰 $20,000,000 Series B over 3 years agoArtificial Intelligence (AI)Machine LearningInsurTechInsuranceHealth Care

⏳ Experience: 7+ years

Requirements:
  • BS in Computer Science or a related quantitative discipline.
  • 7+ years of working experience in data engineering.
  • 2+ years managing engineers in a hybrid/remote setting.
  • Fluency in SQL and experience with non-relational/alternative databases.
  • Experience with Python, Airflow, and Databricks in a professional environment.
  • Management experience is preferred.
  • Desire to learn new tools such as Redshift, Tableau, and AWS services.
  • Exposure to a cloud-computing environment like AWS EC2.
  • Comfortable with Linux, including developing shell scripts.
  • Experience in Insurtech or on AI/ML products is a bonus.
Responsibilities:
  • Manage a team of data engineers in their day to day.
  • Design, build, and implement data systems that fuel our ML and AI models.
  • Develop tools to extract and process client data from different sources.
  • Work cross functionally with data scientists to transform large amounts of data.
  • Contribute to production operations, data pipelines, workflow management, and reliability engineering.
  • Build the infrastructure required for optimal extraction, transformation, and loading of data using SQL and AWS technologies.
Apply

Related Jobs

Apply

📍 US

💸 204000 - 259000 USD per year

🔍 Hospitality / Technology

  • 5+ years of engineering management experience.
  • 9+ years of relevant software development experience in a fast-paced tech environment.
  • Flexible leadership style adaptable to different situations.
  • Experience with user-facing product surfaces, especially regarding search.
  • Strong product and design sense, able to identify usability issues.
  • Strong communication skills and ability to influence decisions.
  • Technical leadership expertise in fullstack development.
  • Experience in leading cross-platform teams, with performance optimization experience considered a bonus.
  • Ability to foster an inclusive team environment.
  • Capability to identify, retain, grow, and acquire critical talent.

  • Define and execute technical direction and vision for the Trust data engineering team.
  • Raise the quality bar for data assets.
  • Identify strategic areas to improve data accessibility for Trust stakeholders.
  • Collaborate and influence data community practices across Airbnb.
  • Collaborate with various teams to address customer pain points.
  • Guide technical direction to enhance data-driven decision-making.
  • Optimize team operations to increase productivity and reliability.
  • Hire and develop team talent.
  • Champion Airbnb’s culture of belonging.

LeadershipSoftware DevelopmentStrategyData engineering

Posted about 1 month ago
Apply
Apply
🔥 Data Engineering Manager
Posted about 1 month ago

📍 Poland. Serbia. Hungary. Spain. Portugal

🧭 Full-Time

🔍 Home improvement

🏢 Company: HomeBuddy👥 101-250Home ServicesHome ImprovementMarketing

  • 5+ years of experience in data engineering, with 2+ years in a leadership role.
  • Strong experience with Python, SQL, and Snowflake.
  • Familiarity with AWS Services like Lambda, Glue, and S3.
  • Knowledge of data governance, security, data modeling principles, and data quality monitoring.
  • Experience with DataOps practices such as orchestration and CI/CD.
  • Excellent communication skills for both technical and non-technical audiences.
  • Strong analytical skills and business acumen.
  • Business fluency in English.

  • Architect and oversee data infrastructure to support advanced analytics, real-time insights, and machine learning.
  • Create high-performance data pipelines integrating diverse data sources for real-time and batch processing.
  • Collaborate with Data Science, Engineering, and Product teams for impactful data-driven solutions.
  • Establish data engineering standards focusing on governance, security, and compliance.
  • Guide the data engineering team through code reviews and foster a high-performing culture.
  • Design and maintain ETL/ELT processes ensuring data accuracy and accessibility.
  • Lead planning and prioritization for data engineering initiatives focusing on ROI and long-term value.

AWSDockerLeadershipPythonSQLETLMachine LearningSnowflakeAirflowData engineeringCommunication SkillsAnalytical SkillsCollaborationCI/CDComplianceData modeling

Posted about 1 month ago
Apply

Related Articles

Posted 4 months ago

Insights into the evolving landscape of remote work in 2024 reveal the importance of certifications and continuous learning. This article breaks down emerging trends, sought-after certifications, and provides practical solutions for enhancing your employability and expertise. What skills will be essential for remote job seekers, and how can you navigate this dynamic market to secure your dream role?

Posted 4 months ago

Explore the challenges and strategies of maintaining work-life balance while working remotely. Learn about unique aspects of remote work, associated challenges, historical context, and effective strategies to separate work and personal life.

Posted 4 months ago

Google is gearing up to expand its remote job listings, promising more opportunities across various departments and regions. Find out how this move can benefit job seekers and impact the market.

Posted 4 months ago

Learn about the importance of pre-onboarding preparation for remote employees, including checklist creation, documentation, tools and equipment setup, communication plans, and feedback strategies. Discover how proactive pre-onboarding can enhance job performance, increase retention rates, and foster a sense of belonging from day one.

Posted 5 months ago

The article explores the current statistics for remote work in 2024, covering the percentage of the global workforce working remotely, growth trends, popular industries and job roles, geographic distribution of remote workers, demographic trends, work models comparison, job satisfaction, and productivity insights.