... | Reference Code: req23377 A Data Engineer for Smart Operations (Global) is responsible to lead the design, development, and maintenance of enterprise-level data architecture and engineering solutions that enable scalable, secure, and efficient data access across Linde's global operations. By building robust data platforms ...
... Posted 2 weeks ago Job Be an early applicant About the job Job Purpose: Design and implement scalable data engineering and data warehouse solutions while managing data schemas, SQL query tuning, and code reviews. Who You Are: - 5+ years of experience in Data Engineering, with strong knowledge of Data Platforms and Data Warehousing ...
... environments (preferably AWS). Document architecture, data flow, and support runbooks; continuously improve platform performance and resilience. Integrate with customer data platforms and pipelines, including bespoke data frameworks. 4–8 years of experience in data engineering or backend development in data-intensive environments. ...
... Responsibilities Develop, test, and maintain scalable Python applications. Work with large datasets to extract, transform, and analyze data. Build and optimize data pipelines and workflows. Perform data cleaning, validation, and preprocessing. Collaborate with cross-functional teams including data analysts and engineers. ...
... members is maximized throughout the year Desired Skills: - Good level of proficiency in a structured programming language, e.g. Python, R. - Experience designing data science solutions to business problems - Deep understanding of ML algorithms for common use cases in both structured and unstructured data ecosystems. - Comfortable ...
... platforms Design & deliver data warehousing solutions and key data engineering workstreams for any required solution. Support cross-functional teams across the data space. 3+ years of experience designing and building scalable distributed data pipelines and dimensional data models - 3+ years of experience in Python and SQL ...
... https://jobeax.com/link/MJvWAZuK638tHuBb RESPONSIBILITIES : Tech Stack & Skills : - Experience in model development using Python/PySpark libraries. Development on Databricks or Dataiku DSS is a plus.- Strong experience on Spark with Scala/Python/Java.- Proficiency in building, training, and evaluating state-of-the-art machine ...
... orchestrate datasets. Perform routine data monitoring, quality checks, and troubleshooting to maintain data integrity and workflow performance. Collaborate with data analysts, data scientists, and senior engineers to deliver data solutions. Document workflows, dataset schemas, and data engineering best practices. Required ...
... drives / memory / transceivers and other hardware related tasks, as required by our server and network teams. Assisting with planning equipment moves or new data center build-outs. Coordinating the packing, shipping, and logistics of equipment to and from remote colocation sites. Maintaining data center documentation. ...
... Ensure the reliability, scalability, and efficiency of data pipelines for ingestion, transformation, and storage. Work with cross-functional teams to understand data needs and deliver high-quality solutions. Troubleshoot and resolve data pipeline issues in production environments. Apply data quality and governance best practices, ...
... Own end-to-end delivery of data products—from raw source data ingestion through transformation to governed, consumption-ready datasets. Collaborate with the Data Platform team on pipeline integration, CI/CD workflows, and adherence to shared coding and deployment standards. - Data Quality & Analysis: Implement data quality ...
... MAKE AN IMPACT Job Title: Data Engineer _DEPS Experience: 3–5 Years Location: Bangalore and Pune (Hybrid – Client Office) Job Summary: Seeking a skilled Senior Data DevOps Engineer having experience in Cloudera platforms, data engineering, and DevOps automation. The ideal candidate will manage and optimize Cloudera environments, ...
... efficient code using Scala and Spark - Work with Azure or on-premises Hadoop ecosystem for data processing - Solve real-world engineering problems related to data infrastructure - Collaborate with cross-functional teams to deliver data-driven solutions - Ensure data quality, reliability, and performance across data systems ...
... Python. Develop and optimize large-scale data transformations using PySpark. Ensure data quality, reliability, and performance of data pipelines. GCP BigQuery Data Ingestion & ETL/ELT Data Pipeline Orchestration Python PySpark Strong SQL and data engineering fundamentals hands-on experience in GCP-based data platforms.
... powering self-serve analytics. Write performant Spark/PySpark and SQL; optimize partitioning, storage formats, and query cost. Data Quality & Reliability (10%) Own data quality: validation, freshness/SLA monitoring, and observability so bad data is caught before it reaches consumers. Make the data layer debuggable: lineage, tests, ...
... an enterprise level : - Proven track record of high-level technical leadershipRequired Skillset :- Demonstrated expertise in designing and deploying complex data architectures within Microsoft Fabric and the broader Azure ecosystem.- Advanced proficiency in Python and SQL for building sophisticated data processing pipelines ...
... Docker, Cloud-Native Infrastructure, CI/CD pipelines. Industry Context: Data engineering experience in Banking Risk, Retail Products, Cards, Mortgage, Deposits, or Wealth Management. Assumed Requirements / Certifications: Databricks Certified Data Engineer, AWS Certified Data Analytics, or Azure Data Engineer Associate.
... of data security and compliance practices: field-level encryption, IAM least-privilege, PII classification, and audit trail design - Experience setting team-level standards: data contracts, schema registries, lineage tracking, or data quality frameworks Hands-on experience building data infrastructure for LLM fine-tuning, ...
... attention to detail and accuracy Ability to follow instructions precisely Reliable, consistent, and organised work approach Comfort with repetitive and structured data tasks Performance Indicators Accuracy of data entry Timeliness of updates Organisation and cleanliness of trackers Reduction in data corrections What You Can ...