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Lead the design and development of scalable data processing applications using Python, PySpark, Databricks, AWS, and Kafka—90% hands-on engineering with 10% team mentoring.
Data Engineer building scalable batch and streaming data pipelines on AWS Cloud using Databricks, Airflow, Python, PySpark, and Kafka at a TCS Bangalore office.
The AI Data Engineer III will architect and implement data platform solutions, focusing on automating data operations and integrating LLM-augmented capabilities like RAG pipelines. The role involves collaborating with cross-functional teams to build scalable data infrastructure and AI-driven self-service tools for a global mobility platform.
Senior Data Engineer building near real-time analytics and streaming platform solutions on AWS (Kinesis, Kafka, Flink, Redshift) using Scala/Python/SQL for a cloud communications SaaS company.
Overview Join to apply for the Snowflake Data Engineer (Python, ETL, SQL) REMOTE UK role at Akkodis This range is provided by Akkodis. Your actual pay will be based on your skills and experience — talk with your…
the company is a unicorn, AI-powered customer communications platform used by 22,000+ companies worldwide to drive revenue, resolve issues faster, and scale customer-facing teams. We’re redefining customer…
First senior data hire at a pre-Series-A AI startup automating property management admin, owning end-to-end data architecture—Python, Postgres, Kafka, and vector databases—building batch and real-time pipelines from near-scratch.
Builds and maintains the data pipelines and analytics infrastructure for Toss’s offline payments business, using Snowflake, dbt, Airflow, and SQL to create a single source of truth for product teams.
Build and maintain Toss Income’s data marts and semantic layer using dbt and Snowflake, ensuring consistent metrics and high-quality data for analytics and AI agents.
Designs and builds enterprise-wide feature pipelines (ML inputs, search indexes, APIs) for Toss, ensuring data lineage, quality, and lifecycle management while standardizing feature metadata and governance processes.
Builds and operates financial-data products for Toss Core, designing scalable DW models and BI strategies to unify revenue, cost, asset, and liability data across the org.
Designs and maintains data models for Toss’s commerce, ads, payments, growth, and business domains, ensuring reliable, standardized data for analytics and product development using SQL, Python, and Hadoop.
Designs and operates scalable data pipelines (ingestion, streaming, batch) for real-time user behavior analytics, enabling targeting, recommendations, and performance tracking in a fintech platform. Core techs: Spark, Kafka, Flink, Airflow, and distributed storage (HBase, Cassandra).
Build and operate ML/LLM platforms for a Korean neobank, focusing on stable, scalable, and secure model training, deployment, and serving using tools like MLflow, Kubeflow, Triton, and vLLM.
Builds and operates a machine-learning platform for a securities app, focusing on LLM serving, gateway systems, and MLOps tooling in Kubernetes.
Designs, builds, and operates scalable data pipelines for AI/ML products in banking, ensuring reliable batch processing, feature engineering, and model inference workflows using distributed systems like Spark and Airflow.
Build and maintain Toss Securities' data warehouse and ETL pipelines using Hadoop, Spark, and Airflow to enable efficient data processing and analytics for a growing fintech platform.
Builds and maintains Toss’s enterprise data warehouse, automating pipelines and data marts with Hadoop and open-source tools while collaborating with analysts and ML teams.
Builds and maintains a global financial data platform for securities, processing real-time market data with AI/ML pipelines using Kafka, Spark, and Iceberg.
Lead QA & Automation Engineer designing scalable test frameworks for data pipelines, APIs, and data products using SQL, Great Expectations, Soda, Airflow, and cloud tools within CI/CD pipelines.
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