Sr. Analytics Engineer at Hims & Hers

Lead and mentor data engineering teams

Architect and deliver scalable cloud data solutions

Deliver scalable data engineering solutions for enterprise clients.

Design and build cloud-native data pipelines and server-side applications.

Deliver customer big data projects

Design and deploy data architectures

Design and optimize scalable AI/ML models

Integrate AI solutions across business units

Deliver client big data projects using Databricks

Lead and manage technical teams

Lead design and implementation of data solutions

Mentor and guide engineering teams

Lead and mentor distributed data engineering teams

Drive strategic direction and execution of data platforms

Lead and mentor data engineering teams

Design and implement scalable data solutions

Lead and scale ML-driven product features

Develop and optimize AI-first user experiences

Grow and lead the ML Practice team in India

Drive successful customer ML and GenAI engagements

Develop scalable data pipelines and solutions

Support enterprise digital modernization projects

Operate and support cloud data platforms

Resolve and prevent data pipeline issues

Lead and mentor distributed data engineering teams

Drive strategic direction for scalable data platforms

Collaborate with clients and stakeholders

Maintain and optimize data pipelines

Lead and mentor a data science team

Integrate analytics into business strategy

Design and productionize scalable machine learning systems

Personalize user experiences using data-driven models

Lead and deliver high-impact cloud data solutions

Engage and advise senior stakeholders strategically

Design and implement scalable data solutions

Lead and mentor engineering teams

Support analytics and engineering projects

Develop and implement data models

Deliver secure and scalable data solutions

Build and optimize data processing pipelines

Lead enterprise-wide data platform migration programs

Ensure compliance with healthcare data regulations

Design and implement scalable Databricks data architectures

Lead migration from legacy data systems to Lakehouse platforms

Establish robust QA strategies for data platforms

Lead testing of data migrations to Databricks

Develop scalable cloud-based big data pipelines

Collaborate with cross-functional and distributed teams

Deliver scalable data engineering solutions

Support enterprise digital modernization

Lead and manage client engagements on database and data engineering projects.

Translate client needs into technical requirements.

Architect scalable data models and ETL pipelines

Design and launch self-serve analytics products

Building and scaling data pipelines for large volumes of data processing

Implementing ETL processes for data integration

Build and productionize Machine Learning models for Apollo products

Optimize users' experience at all stages of their product journey

Lead data science team for business analytics

Develop scalable reporting solutions

Lead QA strategy and execution for data platforms

Establish testing frameworks and methodologies

Design and implement data solutions

Lead and mentor a team

Design and develop best-in-class cloud platform

Develop solutions across data and analytics stack

Lead design and development of data architecture

Implement scalable and reliable data pipelines

Build and deploy ML models for Apollo products.

Enhance user experience through data-driven insights.

Lead a data science team to drive business growth through analytics.

Establish scalable reporting solutions and best practices in data analysis.

At least 4-5 years of experience in Data Engineering, Expertise in Azure cloud service platform.

Good knowledge in Azure services : Azure Data Factory, Azure SQL Database, Azure Key Vault, Azure OpenAI Service.

The Wikimedia Foundation is seeking a Senior DBA. Our objective is to make the sum of all human knowledge available to everyone, and we persist most of this knowledge in MariaDB.

Implementation, maintenance and troubleshooting of relational database systems in production and staging environments.

- Design and implement scalable data pipelines with AWS and Databricks - Optimize relational and

-relational databases using SQL and MongoDB - Utilize Kafka for real-time data streaming and