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Lead Data Engineer designing and developing scalable batch and streaming data pipelines using Apache Spark, PySpark, and Databricks, with data modeling and integration responsibilities to support BI and analytics tools.
Lead Data Engineer architecting scalable data pipelines and ELT processes using Databricks, Apache Spark, and Delta Lake, while mentoring a team of engineers at a data-focused technology services company.
Senior Data Engineer leading BI Analytics & DWH initiatives—designing ETL/ELT pipelines, data models, and scalable data platforms using Snowflake, Databricks, Azure Data Factory, and Python, while mentoring engineers and partnering with BI teams.
Senior Data Engineer building and optimizing large-scale data pipelines on cloud platforms (Snowflake, Databricks) using SQL and Python, with a focus on ETL/ELT implementation and modernization in Hyderabad.
Build and maintain scalable cloud data platforms using Azure and Databricks, developing ETL/ELT pipelines with SQL and Python/PySpark in a senior individual contributor role.
Design, develop, and maintain scalable ETL/ELT data pipelines using PySpark, process large datasets, build data workflows, and maintain data models, data lakes, and data warehouse solutions.
Databricks-focused Data Engineer/Solution Architect embedded in client teams to architect, build, and optimize enterprise Databricks Lakehouse platforms using Medallion Architecture, Spark, Delta Live Tables, Unity Catalog, and CI/CD pipelines across multiple concurrent engagements.
Senior Data Engineer leading enterprise-scale data engineering initiatives, designing data warehouses and data lakes on AWS, and building ETL/ELT pipelines with PySpark, Apache Airflow, and SQL.
Design and develop scalable data engineering pipelines using Databricks, PySpark, Python, and SQL, building data ingestion, transformation, and analytical data models with a focus on big data processing and performance optimization.
Design, build, and maintain scalable cloud-based data pipelines using Databricks, Apache Spark, Python, and SQL for Energy Trading & Risk Management clients.
Senior Data Engineer leading the re-engineering and validation of an existing Azure Synapse enterprise data platform and driving its migration to Databricks, focused on reverse-engineering complex pipelines, ensuring data governance, and implementing lakehouse architectures.
GCP Data Engineer designing, developing, and optimizing scalable cloud-based data pipelines using Google Cloud Platform, BigQuery, DBT, and Apache Airflow. The role requires 5+ years of experience in data engineering and expertise in building ELT/ETL pipelines.
Senior Data Engineer building data reconciliation frameworks and ETL/ELT pipelines to ensure financial data integrity across multiple systems, primarily using SQL and data warehousing technologies.
Senior Data Engineer designing, building, and optimizing scalable ETL/ELT pipelines using PySpark, SQL, and Python, with a strong emphasis on Dataiku (DataIQ) integration and cross-functional stakeholder collaboration.
Senior Data Engineer building scalable GCP data pipelines end-to-end—from design through production support—using BigQuery, Python, SQL, Cloud Composer/Airflow, Dataflow/Apache Beam, and Dataform/dbt.
Lead a team of data architects and engineers, owning end-to-end solution architecture for AB InBev's global data platform using Azure/AWS big data stack, Spark, SQL, and containerized microservices.
Build and optimize scalable data pipelines and ETL/ELT workflows for large datasets using Python and SQL on GCP/AWS, focusing on identity resolution and ID graph systems.
Senior Data Engineer on a 6-month contract building and maintaining automated ETL/ELT data pipelines for data governance and analytics, primarily using Azure Databricks, Azure Data Lake, and Microsoft Purview.
Design and deliver data pipelines and ETL/ELT processes for client-facing analytics, architecting and optimising data models and transformations in Databricks using Python, Spark, and SQL at scale.
Lead a team of 8-10 data engineers while spending 30-50% of time hands-on building ETL/ELT pipelines using PySpark, SQL, and Azure cloud, working with enterprise data sources like SAP.
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