Posted:17 hours ago|
Platform:
On-site
Full Time
General Skills & Experience: Minimum 10-18 yrs of Experience • Expertise in Spark (Scala/Python), Kafka, and cloud-native big data services (GCP, AWS, Azure) for ETL, batch, and stream processing. • Deep knowledge of cloud platforms (AWS, Azure, GCP), including certification (preferred). • Experience designing and managing advanced data warehousing and lakehouse architectures (e.g., Snowflake, Databricks, Delta Lake, BigQuery, Redshift, Synapse). • Proven experience with building, managing, and optimizing ETL/ELT pipelines and data workflows for large-scale systems. • Strong experience with data lakes, storage formats (Parquet, ORC, Delta, Iceberg), and data movement strategies (cloud and hybrid). • Advanced knowledge of data modeling, SQL development, data partitioning, optimization, and database administration. • Solid understanding and experience with Master Data Management (MDM) solutions and reference data frameworks. • Proficient in implementing Data Lineage, Data Cataloging, and Data Governance solutions (e.g., AWS Glue Data Catalog, Azure Purview). • Familiar with data privacy, data security, compliance regulations (GDPR, CCPA, HIPAA, etc.), and best practices for enterprise data protection. • Experience with data integration tools and technologies (e.g. AWS Glue, GCP Dataflow , Apache Nifi/Airflow, etc.). • Expertise in batch and real-time data processing architectures; familiarity with event-driven, microservices, and message-driven patterns. • Hands-on experience in Data Analytics, BI & visualization tools (PowerBI, Tableau, Looker, Qlik, etc.) and supporting complex reporting use-cases. • Demonstrated capability with data modernization projects: migrations from legacy/on-prem systems to cloud-native architectures. • Experience with data quality frameworks, monitoring, and observability (data validation, metrics, lineage, health checks). • Background in working with structured, semi-structured, unstructured, temporal, and time series data at large scale. • Familiarity with Data Science and ML pipeline integration (DevOps/MLOps, model monitoring, and deployment practices). • Experience defining and managing enterprise metadata strategies.
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