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ENVIRONMENT: PLAY a key role in delivering trusted data products that enable investment, operational, and client-facing decision-making across an innovative Investment Management firm seeking a skilled, curious, and…
Senior Java engineer leads large-scale data migrations and builds petabyte-level ETL pipelines using Java, Spark, and cloud platforms, while mentoring teams and ensuring data integrity.
Lead the design and build of enterprise-scale data platforms using cloud tools (AWS/Azure), SQL, Python, Spark, and orchestration (Airflow) to deliver reliable, analytics-ready data for BI, AI, and reporting.
Lead the design and delivery of enterprise-scale data platforms, building scalable ETL/ELT pipelines and cloud-native warehouses to power analytics, BI, and AI across the business.
Designs and maintains serverless AWS data pipelines (Lambda, S3, SQS, EventBridge) and CloudFormation IaC, builds Athena datasets for analytics, and creates QuickSight dashboards including SOX compliance reports.
Lead a team to build and scale cloud data pipelines using Python, PySpark, and AWS for enterprise analytics and reporting.
Lead the design and delivery of scalable cloud data pipelines and platforms, mentor engineers, and enforce data governance and DevSecOps practices for a retail/FMCG company.
Build and maintain scalable data pipelines using Databricks, Apache Spark, and Azure, automating workflows and CI/CD to deliver reliable, high-performance data solutions.
Build and operate cloud-native data platforms using Python, Spark, and AWS/GCP, designing scalable ETL/ELT pipelines and modern data warehouses.
Build and maintain scalable Azure data pipelines (batch & real-time) using Python, PySpark, and Azure services like Synapse and Databricks to power analytics and ML.
Build and maintain data-driven analytics platforms using Python, AWS, and BI tools; develop full-stack solutions with React/Angular and ETL pipelines.
Build and scale Python applications that integrate ML models, using PySpark and Kubernetes; collaborate with data scientists to deploy end-to-end ML pipelines.
Design and optimize Databricks-based ETL/ELT pipelines and streaming systems using PySpark, Delta Lake, and cloud stacks to deliver scalable, secure data solutions for enterprise clients.
Build and enhance enterprise data platforms using PySpark, SparkSQL, and Azure for investment data processing and analytics.
Designs and maintains AWS-based data pipelines, builds scalable ETL/ELT processes, and ensures reliable data delivery for analytics and reporting in a hybrid work setup.
Design and implement scalable AI/ML platforms on Databricks Lakehouse, leading data engineering, MLOps, and generative AI solutions for enterprise use cases.
Lead a team of 10–15 engineers to design and scale cloud-native data platforms using Databricks, GCP, and PySpark, while setting engineering standards and security guardrails for enterprise-wide data transformation.
Designs and builds Spark/PySpark pipelines on Azure Lakehouses to ingest, transform, and store investment data for analytics and downstream systems.
Lead a team to design and build scalable Azure-based data pipelines using Synapse, Fabric, and PySpark, while mentoring engineers and driving best practices in cloud data engineering.
Lead the design and build of a modern Azure-based data platform, owning scalable pipelines, governance, and mentoring engineers to power enterprise analytics and ML.
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