Harman

Experienced Project Manager specializing in retail data engineering and platform implementations. • This role demands adeptness in project and program management, along with expertise in Azure data engineering and Snowflake technologies.

 • As a data engineering project manager, you will be responsible for leading and coordinating data engineering projects from start to finish.

• You will work with stakeholders, such as business analysts, data scientists, and product managers, to define the project scope, objectives, and deliverables.

 • You will also work with data engineers, data architects, and data analysts, to design, develop, and test data solutions that meet the project requirements and standards.

 • Moreover, you will monitor and control the project progress, quality, and risks, and report on the project status and outcomes.

Data Strategy and Alignment

• Work closely with data analysts and business / product teams to understand requirements and provide data ready for analysis and reporting.

• Apply, help define, and champion data governance: data quality, testing, documentation, coding best practices and peer reviews.

• Continuously discover, transform, test, deploy, and document data sources and data models.

• Work closely with the infrastructure team to build and improve our Data Infrastructure.

• Develop and execute data roadmap (and sprints) - with a keen eye on industry trends and direction. Data Stores and System Development

• Design and implement high-performance, reusable, and scalable data models for our data warehouse to ensure our end-users get consistent and reliable answers when running their own analyses.

• Focus on test driven design and results for repeatable and maintainable processes and tools.

 • Create and maintain optimal data pipeline architecture - and data flow logging framework.

• Build the data products, features, tools, and frameworks that enable and empower Data, and Analytics teams across Porter. Project Management

• Drive project execution using effective prioritization and resource allocation.

• Resolve blockers through technical expertise, negotiation, and delegation.

• Strive for on-time complete solutions through stand-ups and course-correction. Team Management

• Manage and elevate team of 10-15 members.

• Do regular one-on-ones with teammates to ensure resource welfare.

• Periodic assessment and actionable feedback for progress.

• Recruit new members with a view to long-term resource planning through effective collaboration with the hiring team. Process design

 • Set the bar for the quality of technical and data-based solutions the team ships.

• Enforce code quality standards and establish good code review practices - using this as a nurturing tool.

• Set up communication channels and feedback loops for knowledge sharing and stakeholder management.

• Explore the latest best practices and tools for constant up-skilling. Data Engineering Stack

• Analytics: Python / R / SQL + Excel / PPT, Google Colab

• Database : PostgreSQL, Amazon Redshift, DynamoDB, Aerospike

• Warehouse: Snowflake, S3

• ETL : Airflow + DBT + Custom-made Python + Amundsen (Discovery)

• Business Intelligence / Visualization : Metabase + Google Data Studio

• Frameworks : Spark + Dash + StreamLit

• Collaboration : Git, Notion Qualification Prerequisites

• Industry experience of minimum 9 years (5 years+ in data engineering role)

• Experience managing a team of at least 4 developers end-to-end

• Strong hands-on data modeling and data warehousing skills

• Strong technical background and ability to contribute to design and review

• Strong experience applying software engineering best practices to data and analytics scope (e.g. version control, testing, and CI/CD)

• Strong attention to detail to highlight and address data quality issues

• Excellent time management and proactive problem-solving skills to meet critical deadlines • Familiarity (expertise preferred) with our current or a similar analytics stack

Data Strategy and Alignment
• Work closely with data analysts and business / product teams to understand requirements and provide data ready for analysis and reporting.
• Apply, help define, and champion data governance : data quality, testing, documentation, coding best practices and peer reviews.
• Continuously discover, transform, test, deploy, and document data sources and data models.
• Work closely with the Infrastructure team to build and improve our Data Infrastructure.
• Develop and execute data roadmap (and sprints) - with a keen eye on industry trends and direction.

Data Stores and System Development
• Design and implement high-performance, reusable, and scalable data models for our data warehouse to ensure our end-users get consistent and reliable answers when running their own analyses.
• Focus on test driven design and results for repeatable and maintainable processes and tools.
• Create and maintain optimal data pipeline architecture - and data flow logging framework.
• Build the data products, features, tools, and frameworks that enable and empower Data, and Analytics teams across Porter.

Project Management
• Drive project execution using effective prioritization and resource allocation.
• Resolve blockers through technical expertise, negotiation, and delegation.
• Strive for on-time complete solutions through stand-ups and course-correction.

Team Management
• Manage and elevate team of 10-15 members.
• Do regular one-on-ones with teammates to ensure resource welfare.
• Periodic assessment and actionable feedback for progress.
• Recruit new members with a view to long-term resource planning through effective collaboration with the hiring team.

Data Engineering Stack
• Analytics : Python / R / SQL + Excel / PPT, Google Colab
• Database : PostgreSQL, Amazon Redshift, DynamoDB, Aerospike
• Warehouse : Snowflake, S3
• ETL : Airflow + DBT + Custom-made Python + Amundsen (Discovery)
• Business Intelligence / Visualization : Metabase + Google Data Studio
• Frameworks : Spark + Dash + StreamLit
• Collaboration : Git, Notion

Qualification Prerequisites
• Industry experience of minimum 12 years (5 years+ in data engineering role)
• Experience managing a team of at least 4 developers end-to-end
• Strong hands-on data modeling and data warehousing skills
• Strong technical background and ability to contribute to design and review
• Strong experience applying software engineering best practices to data and analytics scope (e.g. version control, testing, and CI/CD)
• Strong attention to detail to highlight and address data quality issues
• Excellent time management and proactive problem-solving skills to meet critical deadlines
• Familiarity (expertise preferred) with our current or a similar analytics stack