data engineering , snowflake , Sql, Gcp, Python, AWS, Data Quality, Data Architecture, Data Governance, Data Modeling, Airflow, dbt, cloud-based data platforms

containerization , Storm, Orchestration, Cassandra, Kafka, Informatica, Spark Core, Azure Cloud, Sqoop, Talend, Python, Java, Flume, Scala, Dynamodb, HBase, Kubernetes, Big Data Architecture Patterns, Flink, Pub Sub, Big Table, CI CD pipelines, Pulsar, NiFI

Databricks, LangChain, Databricks SQL, GenAI, cross-team dependency management, vector databases, Delta Live Tables, Unity Catalog, Capacity Planning, cloud platform budgets, Delta Lake, cost-efficiency accountability, Databricks Genie, OKRs, Databricks Workflows

data engineering , snowflake , Databricks, Azure, ELT, Etl, AWS, Ml, BI, Agile Scrum, Automation, Data Governance, Predictive Analytics, Prince2, AI frameworks, Data Lakes, Ai, Analytics, Pmp

BigQuery, Hadoop Ecosystem, Scala, Apache Spark, Kafka, Data Modeling, Dataproc, ELT, Cloud Storage, DataFlow, Etl, Airflow, pipeline orchestration

Power Bi, Azure Sql, Sql, Python, Git, Machine Learning, Data Architecture, Azure, Azure DevOps, Automated Testing, Microsoft Fabric, Generative AI, ERP integration, Ai, CI/CD, Microsoft Dynamics 365 Finance Operations

object storage , Java, Mqtt, Rust, PostgreSQL, concurrency, Kafka, Networking, Storage, Iot, Kubernetes, data-intensive production systems, failure recovery, ClickHouse, Go, backpressure, high-cardinality time-series data, Pulsar, telematics systems, event-processing systems, Performance Analysis

Java, S3, Hadoop, Scala, Nodejs, Data Modeling, Emr, Data Governance, Redshift, Sql, Hive, Ec2, Spark, Python, Semantic layers, GenAI solutions, Dashboard data models, Big data pipelines, AWS Tools and Technologies, ETL pipelines

data engineering , Databricks, Data Warehousing, Azure, Sql

data engineering , Gcp, MLops, Python, Sql, Distributed Computing, Orchestration, Data Governance, Data Lineage, Performance Monitoring, Lakehouse architectures, Automated alerting, MLOps platforms, Observability frameworks, Data quality metrics, Machine learning lifecycle, Data pipelines, CI/CD for machine learning systems