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: • Minimum 8-14 years’ industry experience (with 2-7 years in technical leadership, managing engineering teams in data domains). • Experience architecting and delivering large-scale, complex, enterprise data projects. • Strong leadership, mentorship, and people management skills; ability to motivate highly technical teams. • Demonstrated proficiency with Agile/Scrum and tools for effective collaboration and delivery (JIRA, Confluence, etc.). • Strategic planning and prioritization skills; ability to manage dynamic requirements and deadlines. • Excellent communication, negotiation, and stakeholder management abilities, with an aptitude for translating technical concepts to business audiences. • Highly data-driven, detail-oriented, and accountable for solution quality and performance.
Job Title
Data Engineer_ Engineering Management