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Data Engineer at NNIT's AI Center of Excellence designing and building enterprise cloud data platforms—ETL/ELT pipelines, data lakes, and lakehouses—using Azure, Databricks, Snowflake, Python, and SQL to enable AI and analytics for global clients.
Builds and maintains the AI and data platform that powers Afresh's grocery-focused products, focusing on retrieval, agent systems, and evaluation infrastructure for LLMs.
Senior engineer builds and operates the AI platform that turns raw grocery data into reliable context for LLMs and agent systems, including retrieval layers, evaluation harnesses, and production serving infrastructure.
Senior Cloud Data Engineer leading data storage, processing, and governance initiatives, optimizing cloud data stores/warehouses and designing Delta Lake environments using AWS, Databricks, and IDMC.
Build and scale data infrastructure for traffic and road IT analytics, developing ETL/ELT pipelines, SQL, and Python code on AWS cloud while ensuring data quality and operational excellence across agile squads.
Design, develop, and optimize scalable ETL/ELT data pipelines using Python, PySpark, and SQL for a Singapore-based consulting firm serving banking clients.
Design, build, and optimize cloud data architectures (data lakes, warehouses) on AWS (S3, RDS, Redshift, DynamoDB) and Databricks Delta Lake, ensuring data quality and governance while collaborating with data scientists, analysts, and product teams.
Data Engineer architecting and maintaining cloud-based data analytics infrastructure on AWS, Databricks, and IDMC, designing data lakes, ETL/ELT pipelines, and automation playbooks.
Data Engineer responsible for designing, developing, and maintaining scalable data pipelines on Databricks and Azure, supporting analytics and ML workloads with ETL, streaming, and batch processing.
Lead Data Engineer on a major Federal Government program designing and building enterprise-scale data pipelines and Lakehouse architectures using Databricks, Microsoft Fabric, and Azure cloud technologies.
Data Engineer building scalable ETL/ELT pipelines, data warehouses, and data lakes using Python, SQL, Apache Spark, Airflow, and cloud platforms (AWS/Azure/GCP) to power analytics and AI/ML products.
Staff Data/Platform Engineer designing and scaling distributed data systems (Kafka, Flink, Spark, Delta Lake) for real-time streaming pipelines, analytics, and AI infrastructure at a conversational AI company. Remote in Brazil.
Principal Software Engineer at Recorded Future builds and scales threat intelligence pipelines that collect, transform, and deliver structured security data using Python, Go/Rust, Kafka, and cloud infrastructure.
Lead the design and delivery of real-time data pipelines and backend microservices for InMobi’s DSP, processing billions of bid-stream events daily to power bidding intelligence, audience targeting, and campaign analytics using Python/Java/Scala, Kafka/Flink, StarRocks, and Kubernetes.
We're looking for a technology leader who knows data platforms deeply, has an opinion about the broader data platform landscape, and can turn that conviction into real engagements, real revenue, and stellar…
If you're ready to join a team of talented developers, you belong here. We are seeking Data Engineers to join our Software + Solutions Developer team and work with subject matter experts to develop, construct, and…
The Senior Data Engineer will migrate legacy Python/Spark ETL pipelines to dbt Core models within the Databricks ecosystem. This role focuses on reverse-engineering logic, ensuring data quality through rigorous testing, and collaborating within an engineering pod to modernize reporting infrastructure.
This role involves building and maintaining automated quality-control frameworks for data pipelines and cloud-based analytics platforms within a financial services environment. The engineer will utilize Python, Selenium, PyTest, Azure Databricks, and Spark SQL to perform data validation and API testing.
Senior Python Engineer builds secure, scalable microservices and data pipelines for a bank using FastAPI, PySpark, Azure Databricks, Docker, Kubernetes, Redis, and Kafka.
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