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Build and lead the platform powering SmartThings SmartApps and automation rules, designing scalable AWS services and mentoring engineers while owning features end-to-end.
Overview Data is the beating heart at Holland & Barrett. Our data teams — Data Engineering, Data Warehousing, BI, Data Science and Product Analytics — enable the company's vision of becoming the world leader in…
Build and scale a large-scale event streaming platform using Kafka, Flink, and cloud tools to power AI use cases and data products for a global fintech company.
Build and scale the core data platform for a fast-growing B2B SaaS business, extending Databricks lakehouse, AWS infrastructure, and reusable tooling so teams can move data safely and quickly from ingestion to analytics and AI.
Build and maintain the internal backbone for an AI startup focused on policing, connecting APIs, data pipelines, cloud infrastructure, and ML models using Node, NestJS, Typescript, and AWS services.
Designs and implements cloud-based data architectures (AWS, Azure, GCP) and real-time pipelines (Kafka, Snowflake, Databricks) to align client business goals with modern data solutions.
Design end-to-end big-data architectures (Data Lakehouse, Data Mesh) and scalable ETL/ELT pipelines using Spark, Delta Lake, Iceberg, and cloud platforms (AWS/Azure/GCP).
Designs and implements cloud-based data architectures (AWS/Azure/GCP) and pipelines to improve data integration, quality, and delivery for enterprise clients.
Build and maintain cloud data pipelines and ML infrastructure on Databricks/AWS, collaborating with data scientists and BI teams to enable analytics and machine learning at scale.
Build and maintain cloud-based data pipelines for batch and streaming analytics, using Python, SQL, Kafka, Airflow, and GCP/AWS services to power business insights and ML at a fast-growing Q-commerce platform.
Lead engineer building a real-time financial data hub using Java/Spring Boot and Kafka, migrating from batch to event-driven pipelines while mentoring the team.
Design and build scalable data pipelines and architectures using AWS and Databricks, focusing on PySpark, Python, and SQL to enable advanced analytics and ETL workflows for enterprise clients.
Build and maintain scalable data pipelines and infrastructure for a talent-cloud platform, enabling analytics and AI-driven features using Python, SQL, cloud services, and streaming tech.
Lead the design and scaling of a modern data platform, building robust pipelines and mentoring engineers to ensure reliable, governed data foundations for analytics and reporting.
Build and maintain ETL pipelines to move transactional data into a modern data warehouse, write SQL transformations, and ensure clean, reliable data for analytics and risk teams.
Build and maintain secure, compliant big-data pipelines on AWS, using Python, PySpark, and Terraform to provision analytics-ready datasets for enterprise reporting and governance.
Lead a team to design and implement scalable OTT media platforms, hybrid-cloud microservices, and DevSecOps pipelines that embed security into CI/CD while improving reliability and time-to-market.
Build and maintain cloud data pipelines on AWS, Azure, or GCP to ingest and process data for clients, enabling analytics and decision-making.
Design and build scalable cloud data platforms for a fintech firm, including data warehousing, streaming pipelines, and AI-driven analytics using AWS, dbt, and Python.
Build real-time data products end-to-end in TypeScript/React and help introduce Go for performance-critical services on AWS.
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