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Senior DataOps & Cloud Data Engineer to design, build, and optimize cloud-based data pipelines and lakehouse solutions using Azure Data Factory, Databricks, Informatica, Python, and SQL for a large public-sector modernization project.
Senior Data Engineer designing and developing large-scale data processing and persistence software using big data platforms like AWS, Azure, GCP, and Databricks, primarily with Java, Scala, or Python.
Senior Data Engineer on a remote contract within Canada, building and maintaining scalable data pipelines, ETL/ELT workflows, data models, and Power BI dashboards for the Government of Alberta's Digital Design and Delivery division using Python, SQL, PySpark, and cloud platforms (Azure, Databricks, Microsoft Fabric).
Senior Data Engineer building ELT/ETL pipelines, data models in Snowflake, and Power BI dashboards for BlackLine's AI-powered Invoice-to-Cash SaaS platform.
Senior Data Architect at a supply-chain SaaS platform, responsible for designing enterprise data models, building scalable ETL/ELT pipelines, and translating complex customer ERP data into canonical supply-chain datasets using SQL, Python, and cloud lakehouse technologies.
Lead Data Engineer designing and building scalable real-time and batch data platforms for supply chain workflows using Python, SQL, PySpark, and container orchestration tools.
Evaluate AI-generated data engineering implementations—ETL/ELT pipelines, data warehouses, distributed systems—using frontier AI coding agents, with Python and SQL as core technologies.
Palantir Foundry Data Engineer building scalable data pipelines, Ontology models, Workshop apps, and Quiver dashboards using Python, PySpark, and SQL.
Build and maintain ETL/ELT pipelines using Informatica and Snowflake, develop data models, and manage metadata and data lineage with Informatica Data Catalog to support enterprise data governance and analytics.
Build and maintain data infrastructure—ETL pipelines, cloud data warehouse, and automated reporting—for a private credit investment firm in Gurugram, working with loan-level datasets and integrating servicer/financial data sources using SQL, Python, and cloud warehouses.
Senior Data Engineer designing and building scalable ETL/ELT pipelines, data lakes, and data warehouses on AWS (Lambda, Glue, S3, Redshift, EMR, MWAA/Airflow) with Snowflake and Python across multiple Indian cities.
Data Analyst/Data Engineer designing analytics architecture, managing databases, writing SQL queries, and automating Power BI reports for stakeholders.
GCP Data Engineer building scalable batch and real-time data pipelines on Google Cloud Platform, supporting AI/ML and analytics use cases with BigQuery, Dataflow, Python, and SQL.
Design, develop, and maintain scalable data pipelines and ETL/ELT solutions on GCP using DBT, Terraform, SQL, and large-scale processing frameworks like Spark/Beam/Hive.
GCP Data Engineer building scalable, AI-ready data pipelines and cloud-native data solutions on Google Cloud Platform, collaborating with enterprise architects and analytics teams on large-scale data modernization initiatives.
Hands-on Solutions Architect role designing and building enterprise-grade data platforms on GCP/AWS using Snowflake, Databricks, BigQuery, Airflow, and Kafka to power AI/ML initiatives, with pre-sales responsibilities at a tech consultancy in Bangalore.
Data Analyst/Data Engineer Intern building and maintaining SQL/Python data pipelines on AWS (RDS, Redshift), creating Frappe/ERPNext operational dashboards, and handling REST API integrations — all on-site in Gurugram.
Lead enterprise data platform and analytics initiatives with a focus on Azure Data Factory, Databricks, PySpark, and GenAI/Azure OpenAI solutions, while providing technical leadership and mentoring.
Lead Data Engineer overseeing enterprise-scale ETL and data modernization projects, architecting pipelines with Python, PySpark, Informatica, Databricks, Snowflake, ADF, and AWS Glue while mentoring a team of data engineers.
Data Engineer building and optimizing Big Data pipelines with PySpark, Apache Spark, Airflow, and Python, including Spark performance tuning and CI/CD integration on cloud platforms (GCP preferred).
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