Project Manager - Data Engineering / Technical Project Manager

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.