Data Engineer_ Engineering Management

Experience : 7 Yrs- 15Yrs

Location : Bangalore, Hyderabad, Chennai, Mumbai, Pune, Kolkata, Gurgaon.

Notice Period : 0 to Max 45 Days

General Skills & 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.

Leadership and Management Skills :