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
HARMAN is proud to be an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.