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Job Requisition ID # 26WD101310 Position Overview Autodesk’s Data Platform is building the next generation of data infrastructure, AI native, scalable, and developer-friendly systems that power analytics, AI, real-time…
Senior/founding backend engineer at Zipher, which builds an autonomous execution layer for cloud data and AI workloads. You'll architect and scale distributed, high-throughput backend services that make real-time multi-cloud infrastructure decisions, primarily using Python or Go and AWS services like EMR, DynamoDB, Kinesis, and Kubernetes.
Senior engineer who runs and improves enterprise data platforms on Kubernetes (on-prem and AWS/GCP, similar to Data on EKS), keeping production workloads like Spark, Airflow, and Jupyter reliable and secure. Day to day: GitOps deployments, monitoring/observability, incident response in 24/7 rotations, troubleshooting, and mentoring junior engineers.
Business At Work is hiring an experienced Data Engineer to build data pipelines for a public-sector client in Toulouse. Day to day: designing ETL/ELT pipelines with Talend or Informatica, orchestrating with Airflow, deploying on Kubernetes (Helm, Docker, Argo CD), managing S3/MinIO object storage, and working with BI tools.
Contract role in Singapore for a senior engineer to design, deploy, and operate the compute and transformation layer of a large-scale on-premise/hybrid data platform, containerising services with Docker and running workloads on Kubernetes, Spark, Trino, and ClickHouse.
Contract role deploying and operating the compute and transformation layer of a large-scale on-premise data platform: containerizing services with Docker/Kubernetes, running Spark, Trino and ClickHouse, building dbt workflows over Apache Iceberg/Polaris, and monitoring with VictoriaMetrics/VictoriaLogs. Initial 6-12 month contract in Singapore, with potential extension or permanent conversion.
Senior engineer on Grab's Data Compute Platform infrastructure: designing and operating the multi-tenant Kubernetes (EKS) and AWS foundations - Terraform IaC, GitLab CI/CD, Go-based Kubernetes operators - that run Spark, Ray, Trino, Starrocks and Airflow, with ownership of reliability, security and cost efficiency. Onsite in Petaling Jaya.
Senior data engineer at super.money in Bengaluru owning the full data platform: designing batch and streaming ingestion pipelines, operating ClickHouse at scale, and building a data lake/lakehouse with open table formats as the org's governed source of truth. Core tech: ClickHouse, Kafka, Spark/Trino/Presto, Airflow/Dagster.
Senior Data Engineer at Wise (London) joining the Scalable Growth team to act as the data architecture lead for the marketing platform — building and scaling high-volume streaming/batch pipelines with Python, dbt, Airflow, Kafka, and Trino/Iceberg, owning MarTech infrastructure in-house, and mentoring engineers.
Builds and optimizes large-scale data pipelines using MPP SQL engines — Apache Doris/StarRocks and Trino/Presto — alongside Snowflake, Spark, and lakehouse formats like Iceberg, Delta Lake, and Hudi. Coding in Python, Java, or Scala with Git and CI/CD, with a focus on Parquet/ORC pipeline tuning and cross-platform workload migration.
Infosys Compaz is hiring a Data Engineer in Singapore to build and optimize complex SQL analytics on lakehouse and MPP platforms — working with engines like Doris, Trino/Presto, and Snowflake, and using Spark with open table formats (Iceberg, Delta Lake, Hudi) for workload migration.
On-site data engineering role in Singapore focused on large-scale analytics: optimizing queries, managing data models, and migrating workloads/streamlining pipelines using Apache Doris and lakehouse technologies such as Trino/Presto, Snowflake, Spark, and cloud storage.
About Starburst Starburst delivers enterprise intelligence at scale by giving organizations secure, governed access to all their data, wherever it lives. Built for distributed data environments, Starburst helps…
Senior backend engineer on Rippling's Platform team building RQL (Rippling Query Language), a custom language with its own parser, distributed query engine, and datastore powering analytics, workflows, and reporting across the platform. Day-to-day work centers on distributed systems and big data with Python, MongoDB, Kafka, and tools like Trino/Presto, Flink, Pinot, and Iceberg, based in-office in
Senior Data Engineer at TCS building and operating distributed batch and streaming data pipelines on Google Cloud for enterprise clients. Day to day involves Spark (PySpark/Scala/SQL) lakehouse development on GCS with Iceberg, Kafka/Flink streaming, Airflow orchestration, and CDC/data ingestion, across Pune, Hyderabad, or Bangalore.
A Resident Solution Architect at Starburst works closely with customers to understand their data challenges, then designs and leads the implementation of analytics solutions built on Starburst's Trino/Iceberg-based data platform. Day to day includes architecture design, deployment, troubleshooting, technical guidance, and training partner personnel, with SQL, distributed query engines, cloud platf
Senior engineer on Voleon's Strategy Platform builds and operates the infrastructure between ML research and live trading at an AI-driven asset manager: data ingestion, feature computation, deployment pipelines, and observability. Day to day it's backend/platform engineering in Python, plus data infrastructure like Airflow, Spark, and Flink.
The Staff Software Engineer will lead the development and unification of the strategy platform, which manages data ingestion, feature computation, and deployment for ML-driven trading strategies. This role involves designing and evolving critical infrastructure that bridges quantitative research and live production trading systems.
Data Solution Engineer on Qonto's Data Platform team, owning end-to-end data solutions from ingestion into Snowflake to exposure via Trino and Metabase, building self-service data products for business users. Works with Python, SQL, Airflow, OpenMetadata, Kubernetes, and AWS.
Data Engineer in Singapore specializing in Apache Doris and large-scale analytical query infrastructure. Day to day involves designing and optimizing OLAP workloads in Doris, building lakehouse pipelines with Iceberg and Trino, migrating workloads from Snowflake/Spark, and automating data pipelines with Python/Java/Scala and CI/CD.
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