Principal Engineer - Data Layer

Role & responsibilities

• Architect, Design & Build:

  Define and implement

  enterprise-grade analytical data platforms

  following

  Lakehouse and Data-as-a-Service (DaaS)

  principles.

Hands-On Engineering Leadership:

• Personally develop and validate key platform components and data flows.
• Create POCs and show-by-doing implementations to accelerate team understanding and delivery.
• Optimize complex data integrations, transformations, and performance bottlenecks.
• Lead by example in design, development, and debugging efforts, serving as a "player-coach" for the team.

Data Platform Expertise:

• Design data storage and retrieval layers using

  ClickHouse (Preferred), Apache Druid, Apache Doris and the likes

  ,

  PostgreSQL/MS SQL

  ,

  MongoDB

  , and

  Elasticsearch

  .
• Build and optimize

  data pipelines

  (Apache Seatunnel, AWS Glue, etc.) for large-scale, high-volume data processing.
• Model analytical structures using

  OLAP principles

  ,

  semantic layers

  , and

  data visualization

  frameworks (Apache Superset (preferred) or similar open-source offerings).

• Scalability & Performance:

  Deliver reliable and performant data systems that handle

  diverse, massive, and complex datasets

  .

• Quality & Consistency:

  Set and enforce standards for

  data accuracy

  ,

  governance

  ,

  security

  , and

  performance

  .

• Execution Ownership:

  Manage

  Sprints

  , perform

  SMART task breakdowns

  , and ensure

  on-time, high-quality

  team deliverables.

• ClickHouse

  : Manage and optimize this high-performance OLAP database for real-time analytics on massive datasets. Clickhouse experience is preferred, however experience with other open source OLAPs like Apache Druid/Doris can apply.

• Data Integration

  : Utilize

  Apache Seatunnel

  (or similar tools) for efficient data ingestion and synchronization across various sources.

• AI/ML & Agentic AI

  : Lead the development and integration of AI models, algorithms, and Agentic AI solutions to solve complex business problems and automate processes.

• Databases

  : Manage and optimize both

  PostgreSQL

  (relational) and

  NoSQL

  databases to support diverse data storage needs.

• Data Visualization/BI

  : Implement and manage data visualization and exploration tools like

  Apache Superset

  to deliver actionable insights to stakeholders.

• Infrastructure

  : Oversee deployment and orchestration using technologies like Docker, Kubernetes, and potentially specific environments such as

  MCP servers

  (Model Context Protocol, if applicable to the company's tech stack)

• Data Governance & Quality:

  Ensure robust data governance, integrity, privacy, and security standards are maintained across the platform.

Preferred candidate profile

Data Systems & Storage:

ETL / Data Pipelines:

Modeling & Semantics:

Data Visualization:

Architecture Patterns:

Languages & Tools:

Code Repositories & Build :

Agentic AI:

Cloud Platforms: