Manager | Data Engineering Company Overview At Porter, we are passionate about improving productivity. We want to help businesses, large and small, optimize their last-mile operations and empower them to unleash the growth of their core functions. Last mile delivery logistics is one of the biggest and fastest growing sectors of the economy with a market cap upwards of 50 billion USD and a growth rate exceeding 15% CAGR. Porter is the fastest growing leader in this sector with operations in 14 major cities, a fleet size exceeding 1L registered and 50k active driver partners and a customer base with 3.5M being monthly active. Our industry-best technology platform has raised over 50 million USD from investors including Sequoia Capital, Kae Capital, Mahindra group and LGT Aspada. We are addressing a massive problem and going after a huge market. We're trying to create a household name in transportation and our ambition is to disrupt all facets of last mile logistics including warehousing and LTL transportation. At Porter, we're here to do the best work of our lives. If you want to do the same and love the challenges and opportunities of a fast paced work environment, then we believe Porter is the right place for you. Company URL : https://porter.in/ Responsibilities 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 5-8 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
