Project Manager - Data Engineering / Technical Project Manager (Bengaluru)

Job Description Role: Project Manager - Data Engineering / Technical Project Manager Engagement Type: Full time Employee (FTE) Location: Work from Office Indiranagar, Bengaluru Experience: 5+ Years Company: Nu10 Technologies We are looking for a Technical Project Manager with a strong background in Data Engineering to lead end-to-end delivery of enterprise data platform initiatives. The ideal candidate should have prior hands-on experience in Data Engineering and possess deep technical understanding of modern data architectures, large-scale data pipelines, cloud data platforms, Spark-based processing, and Data Lake implementations. The candidate should be capable of driving delivery while also providing technical direction to engineering teams. Key Responsibilities - Lead end-to-end delivery of Data Engineering and Data Platform projects from planning through production deployment. - Work closely with Data Engineers, Data Architects, Business Analysts, QA teams, and stakeholders to define project scope, timelines, and deliverables. - Review and validate solution architecture for enterprise Data Lake implementations. - Drive implementation of Medallion Architecture (Bronze, Silver, Gold layers) and ensure best practices are followed. - Oversee the design and development of scalable batch and real-time data pipelines. - Review technical designs for ETL/ELT pipelines and ensure performance, scalability, reliability, and maintainability. - Understand and guide Spark/PySpark-based data processing, partitioning, optimization, caching, and performance tuning. - Ensure proper orchestration and scheduling of data workflows using tools such as Airflow, Azure Data Factory, or similar orchestration platforms. - Monitor project risks, technical dependencies, resource planning, and delivery milestones. - Conduct architecture reviews, sprint planning, backlog grooming, and technical discussions with engineering teams. - Coordinate cross-functional teams to resolve technical blockers and delivery risks. - Drive implementation of data quality checks, monitoring, logging, and observability frameworks. - Ensure governance, security, metadata management, and compliance standards are followed across data platforms. - Collaborate with DevOps teams for CI/CD, infrastructure automation, and production deployments. - Communicate project status, risks, dependencies, and technical updates to leadership and business stakeholders. Mandatory Technical Expertise :- The candidate should possess strong understanding of: - Data Lake implementation and modernization - Medallion Architecture (Bronze, Silver, Gold) - Data Warehouse concepts and dimensional modeling - ETL and ELT architectures - Agile/Scrum Delivery - Client Interactions/ Stakeholder Management - Data pipelines - Apache Spark, PySpark, Python - SQL optimization and performance tuning - Data partitioning, repartitioning, bucketing, and file optimization - Data ingestion from databases, APIs, Kafka, files, and cloud storage - Data modeling (Star Schema, Snowflake Schema) - Cloud platforms such as AWS, Azure, or GCP - Data orchestration using Airflow, Azure Data Factory, Glue Workflows, or equivalent - Data Lake technologies such as S3, ADLS, or GCS - Delta Lake, Iceberg, or Hudi concepts - Data Quality, Data Lineage, and Metadata Management - Monitoring, logging, and production support - CI/CD Pipelines Experience - 8+ years of overall IT experience. - Minimum 5+ years managing Data Engineering or Data Platform projects. - Prior hands-on experience as a Data Engineer or Technical Lead. - Experience delivering enterprise-scale cloud data platform implementations. What We Are Looking For The ideal candidate should not only manage project plans but also be technically capable of: - Reviewing Data Engineering solution designs. - Understanding pipeline bottlenecks and Spark optimization. - Challenging engineering decisions with technical reasoning. - Estimating delivery effort based on technical complexity. - Managing risks associated with large-scale Data Engineering implementations. .