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Metropolis

Checkout-free parking payments via computer vision

Director – Data Engineering & Analytics

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • At least 8 years of experience in data engineering, analytics engineering, or data infrastructure roles.
  • At least 3 years of experience managing multi-team or multi-functional data organizations.
  • Deep expertise in modern data stack tools and concepts, including ELT pipelines, cloud data warehouses, dbt, orchestration frameworks, and data modeling.
  • A proven track record of building and scaling data platforms that support AI/ML workloads, including experience with feature stores, vector search, or ML pipeline infrastructure.
  • Strong experience with at least one major cloud data platform such as Snowflake, BigQuery, or Databricks.
  • Demonstrated ability to recruit, develop, and retain high-performing engineers and tech leads.
  • Ability to translate complex technical concepts for business audiences and vice versa.

Responsibilities

  • Lead, mentor, and grow the Data Engineering, Analytics Engineering, and BI Development teams while establishing engineering best practices, career ladders, and performance standards.
  • Define team charters, OKRs, and strategic roadmaps aligned with company-wide goals, and hire, develop, and retain top engineering talent.
  • Own the end-to-end vision, architecture, and execution of the data platform, from ingestion and storage through transformation, serving, and consumption layers.
  • Drive adoption of modern data stack technologies such as dbt, Spark, Airflow, Snowflake, BigQuery, Databricks, and Kafka to ensure scalability, reliability, and performance.
  • Implement data quality, lineage, observability, documentation, governance, and compliance standards in partnership with Security.
  • Partner with Data Science and AI Engineering teams to build AI/ML infrastructure for real-time and batch insights, accelerate model development, and prepare data for generative AI and traditional ML workflows.
  • Oversee analytics engineering and BI development to deliver reliable dbt semantic models, self-serve dashboards, embedded analytics, SLAs, and a data-as-a-product mindset.
  • Partner with executive leadership to translate business needs into data platform investments and collaborate across Engineering, Product, Finance, and Operations.

Desired Qualifications

  • Experience with LLM integration and generative AI infrastructure, such as RAG pipelines, embedding pipelines, and prompt evaluation frameworks.
  • Familiarity with data mesh or data product frameworks.
  • Experience in a fast-growth startup or scale-up environment.
  • Background in analytics, business intelligence, or data science in addition to engineering.

About the Company

Metropolis

Checkout-free parking payments via computer vision

Metropolis is a technology-driven facility-management company that focuses on parking solutions using computer vision to enable checkout-free payments. Its system detects vehicles and parking events via cameras and sensors, allowing drivers to pay automatically without stopping at a gate. The platform integrates into real estate operations, Class A office buildings, multi-garage complexes, and valet services to improve asset productivity and streamline operations. Compared with others, Metropolis supports a wide customer base across North America, operates in over 360 cities, processes more than $4 billion in payments annually, and has grown through significant funding rounds and the acquisition of SP Plus Corporation. Its goal is to expand its platform’s capabilities and market reach, helping property owners and managers increase efficiency and revenue by embedding its payment and facility-management tools directly into daily operations.

Company size
1,001-5,000
Company stage
Series D
Total funding
$3.5B
Headquarters
Santa Monica, CA
Founded
2017

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