Senior Data Scientist, Identity

Design, build & maintain data infrastructure.

Enhance data monitoring & alerting.

Lead end-to-end design and delivery of scalable data products.

Identify automation and integration opportunities.

Architect high-scale data infrastructure

Build ELT/ETL workflows for data lakes and warehouses

Architect, design, and maintain scalable data pipelines

Implement cloud architecture with security and governance

Lead and support a team of engineers

Collaborate with product, design, and analytics teams

Lead and manage a team of engineers

Collaborate with product, design, and analytics departments

Architect high-scale data infrastructure

Design, build, and scale data pipelines

Design and maintain data pipelines from diverse sources

Develop data models for analytics and optimization

Deploy ML risk solutions at scale

Lead technical decisions and influence strategy

Lead and contribute to data engineering infra and pipelines

Manage a team of senior data engineers

Contribute to roadmap and architecture based on tech and business needs

Write well-crafted, well-tested, readable, maintainable code

Lead design and development of real-time bidding systems

Build robust data pipelines for insights

Lead design and implementation of shared data models and dimensions.

Standardize data engineering practices across teams.

Build self-service data experiences for analytics and reporting

Create fault-tolerant data pipelines across microservices and third-party systems

Design and maintain production data models

Implement ELT in a cloud warehouse

Build and maintain data platform capabilities

Contribute to data lake modernization

Drive growth through analytics and modeling

Integrate ML models for product enhancement

Design, build, and maintain scalable data models with SQL and dbt

Apply software engineering practices: versioning, testing, CI

Develop secure, scalable data systems

Build and operate data pipelines

Design and build scalable data pipelines for clinical RWD

Implement Python/SQL ETL/ELT with AWS services

Design and implement data governance policies and procedures.

Build pipelines and warehouses for financial data reporting.

Design and implement IAM policies, audit logging, and scalable access for data governance.

Build and ensure data integrity for financial data infrastructure.

Own critical infrastructure supporting engineering teams

Design, deploy, and operate Kubernetes platforms

Lead data science and ML initiatives

Build and lead a team for predictive modeling and experimentation

Design, build, and maintain analytical data models for brokerage and ledger

Own datasets for client metrics, ops decisions, and regulatory reporting

Lead autonomous data projects

Use SQL for analysis

Lead data projects to enhance analytics capabilities.

Query data warehouses efficiently using SQL.

Run large-scale data warehouses with efficient SQL

Define ETL models with Airflow

Design, build, and maintain production data models

Collaborate with Data Engineering

Design and maintain production data models for incentives, promotions, and analytics.

Partner with Data Eng to model source data and implement auditable ELT.

Build and maintain data infrastructure processing petabytes of data

Write clean, well-tested code for data ingestion, transformation, and serving

Lead technical implementation & optimization of Altimate AI platform

Translate customer needs into technical requirements

Empower developers through technical communication and content creation

Engage with technical communities to promote DataHub

Hiring Python and Kubernetes Specialist Engineers for Data, AI/ML & Analytics Solutions

Building open source solutions for public cloud and private infrastructure

Building APIs and backend systems for products

Integrating with third-party systems

Propose, design, and provision cloud-native data solutions.

Lead a technical team operating modern data platforms.

Empowering developers through technical content and engagement

Promoting DataHub and metadata management ecosystem through events

Hiring Python and Kubernetes Engineers for Data, Workflows, AI/ML, and Analytics solutions

Collaborating on end-to-end data analytics using open-source tools

Engaging with technical communities and fostering positive interactions.

Creating technical content to educate users about DataHub's features and best practices.

Deliver insights and analytics to drive strategic opportunities

Turn messy data into reliable, analysis-ready datasets

Lead technical implementation and optimization of data platform

Serve as primary technical contact for key accounts

Implement data ingestion routines

Develop a workflow orchestration platform

Hiring Python and Kubernetes Specialist Engineers focused on Data, Workflows, AI/ML, and Analytics Solutions

Developing end-to-end data analytics and MLOps solutions with open-source tools

Support developers in the DataHub Slack community.

Create compelling technical content to educate users about DataHub.

Empower developers through technical communication and content creation.

Engage actively in the DataHub community to support users.

Architect scalable, secure data platforms.

Implement modern software engineering practices.

Build open source AI/ML and analytics solutions

Develop and maintain scalable data platforms

Architect scalable data pipelines and infrastructure.

Enable real-time, reliable, and high-quality data access.

Design and implement scalable data architectures

Lead and mentor engineering teams

Empower and support DataHub developers

Create educational technical content