Technical Manager / Data Project Manager

About the Role

This position entails leading and overseeing large-scale Data Engineering and Data Modernization programs. The role demands hands-on management of data-centric projects from start to finish including effort estimation, defining scope, formulating project plans, setting timelines, allocating teams, and maintaining stakeholder communication.

Key Responsibilities

• Lead the delivery of Data Lake creation and migration projects.
• Manage PySpark migration and optimization initiatives focusing on performance and scalability.
• Translate business requirements into effective data solutions, actively addressing risks, challenges, and interdependencies.
• Architect and execute modern data frameworks including Data Lakes, Data Warehouses, and Lakehouse implementations.
• Collaborate with business leaders, architects, and engineering teams to establish data strategies and development roadmaps.
• Provide technical guidance and mentorship to data engineering teams.
• Enforce best practices related to data governance, quality, security, compliance, and performance tuning.
• Drive modernization efforts for data platforms across various cloud setups.
• Review technical designs, architectural proposals, and deployment methods.
• Manage project scope, resource distribution, risk mitigation, and delivery schedules.
• Champion Agile methodologies ensuring effective project execution.

Required Qualifications and Skills

• Proven track record in managing data projects end-to-end including planning, resourcing, and stakeholder engagement.
• Proficient with modern data technologies such as PySpark, SQL, CML, and Python on cloud infrastructures, preferably Google Cloud Platform.
• Experienced in executing Data Lake implementations, migrations, PySpark migration projects, and major data transformation initiatives.
• Technical proficiency in Python, Spark SQL, ETL/ELT frameworks, and Hadoop ecosystem.
• Sound knowledge of data lakes and lakehouse architectural models and distributed data processing.
• Understanding of data modeling, integration patterns, both batch and real-time data processing.
• Familiarity with cloud services like AWS, Azure, and GCP.
• Hands-on experience with data migration tactics, performance optimization, and CI/CD as well as DevOps practices applicable to data platforms.

Preferred Skills

• Experience with Databricks, Delta Lake, Apache Airflow, Kafka, Snowflake, Kubernetes, and Docker.
• Background in banking, financial services, or large-scale enterprise environments.
• Familiarity with frameworks for data governance and data quality.