9 of 9 Permanent Parquet Jobs

Python or Scala. Hands-on use of cloud data stacks (e.g. SageMaker Notebooks, S3, Glue, Athena) and modern storage formats or frameworks (e.g. Parquet, Delta Lake, Fabric). Experience with DevOps/DataOps tooling and practices (e.g. Terraform) and testing data pipelines, including end-to-end, data quality, monitoring ...

Python or Scala. Hands-on use of cloud data stacks (e.g. SageMaker Notebooks, S3, Glue, Athena) and modern storage formats or frameworks (e.g. Parquet, Delta Lake, Fabric). Experience with DevOps/DataOps tooling and practices (e.g. Terraform) and testing data pipelines, including end-to-end, data quality, monitoring ...

Python or Scala. Hands-on use of cloud data stacks (e.g. SageMaker Notebooks, S3, Glue, Athena) and modern storage formats or frameworks (e.g. Parquet, Delta Lake, Fabric). Experience with DevOps/DataOps tooling and practices (e.g. Terraform) and testing data pipelines, including end-to-end, data quality, monitoring ...

data models align with business and technical goals. Leveraging tools such as ERWin, Sparx, RSA, Visio , and handling semi-structured data formats (JSON, XML, Parquet). SKILLS AND EXPERIENCE The successful Data Modeller will have the following skills and experience: Proven experience in data modelling within consulting or financial ...

high-quality production code Strong foundation in operating systems and systems programming Proven track record in performance engineering Familiarity with tools like Apache Arrow, Parquet, DataFusion, Clickhouse, or DuckDB is a plus Understanding of cutting-edge ML infrastructure stack (e.g. PyTorch, CUDA) is also a plus Experience with Rust ...

high-quality production code Strong foundation in operating systems and systems programming Proven track record in performance engineering Familiarity with tools like Apache Arrow, Parquet, DataFusion, Clickhouse, or DuckDB is a plus Understanding of cutting-edge ML infrastructure stack (e.g. PyTorch, CUDA) is also a plus Experience with Rust ...

data Develop resilient, well-structured Python/OOP solutions underpinning tick-data ingestion, normalisation, storage, and replay Design and evolve data-lake patterns using Parquet and related columnar formats; introduce Iceberg-style table modelling where beneficial Implement cloud-backed processing and storage patterns - leveraging services from AWS, Azure … development Demonstrable experience building or maintaining large-scale tick-data platforms or market-data ingestion systems Strong knowledge of data-lake architecture, columnar storage (Parquet essential), and modern table formats (Iceberg highly advantageous) Familiarity with cloud ecosystems (AWS, GCP, or Azure), including compute, storage, and workflow orchestration patterns Comfortable ...

data Develop resilient, well-structured Python/OOP solutions underpinning tick-data ingestion, normalisation, storage, and replay Design and evolve data-lake patterns using Parquet and related columnar formats; introduce Iceberg-style table modelling where beneficial Implement cloud-backed processing and storage patterns - leveraging services from AWS, Azure … development Demonstrable experience building or maintaining large-scale tick-data platforms or market-data ingestion systems Strong knowledge of data-lake architecture, columnar storage (Parquet essential), and modern table formats (Iceberg highly advantageous) Familiarity with cloud ecosystems (AWS, GCP, or Azure), including compute, storage, and workflow orchestration patterns Comfortable ...

direction across Python services, distributed systems, and large-scale data processing. Work heavily with AWS — especially Redshift, Lambda, ECS, S3 , plus data formats like Parquet . Shape high-throughput data pipelines and SQL-based workflows , ensuring scalability and performance. Run technical design reviews , guide architecture decisions, and support engineers … technical requirements, prioritise roadmaps, and drive measurable outcomes. Tech Environment You’ll Influence Primary language: Python AWS stack: Redshift, Lambda, ECS, S3 Data workflows: Parquet, SQL, distributed data pipelines Scale: systems processing hundreds of millions to billions of rows Focus areas: performance tuning, reliability, cost optimisation, architectural clarity What ...