Senior Data & AI Platform Engineer

Key responsibilities:

Key Responsibilities

•            Design and develop scalable backend systems

•            Define and implement data architecture and database design strategies 

•            Build and optimize data ingestion and transformation pipelines 

•            Develop and maintain unified data models across multiple systems 

•            Design end-to-end data flows and integration strategies 

•            Collaborate with cross-functional teams to ensure efficient data handling and processing 

•            Ensure system scalability, performance, and reliability 

Machine Learning & AI Integration

•            Work closely with the AI/ML team to understand data requirements for model training, feature engineering, and pipeline development 

•            Prepare, validate, and deliver clean, structured data to support AI/ML systems and intelligent workflows 

•            Consume and integrate outputs from ML models into data pipelines and backend systems 

•            Maintain awareness of ML concepts to effectively collaborate with AI/analytics teams

Core Technical Skills

•            Backend & System Design

•            Expertise in system scalability and performance optimization 

•            Solid understanding of distributed systems and backend architecture 

•            Data Engineering Concepts (IMPORTANT)

•            Strong knowledge of data modelling (OLTP vs OLAP) 

•            Strong knowledge of data lake architecture (primary) and data warehousing concepts

•            Strong knowledge of REST APIs and API design (FastAPI / Flask / Django)

Core Platforms & Technologies (Required)

Strong working knowledge of:

•            Relational databases - strong hands-on experience required (e.g. PostgreSQL, MS SQL Server, MySQL)

•            Non-relational / NoSQL databases - working knowledge required (e.g. MongoDB, SQLite, TimescaleDB, Redis)

•            Graph databases - understanding of graph data models (e.g. Apache AGE, Neo4j concepts)

•            Comfortable working across multi-database environments - relational, time-series, graph, and cache layers in a single architecture

Good to Have: Hands-on experience with modern data tools such as Kafka, Airbyte, S3/ADLS, Snowflake, or similar platforms

Data Ingestion & Processing

Ability to design and implement: 

•                     Data ingestion pipelines 

•                     Data transformation layers (ETL/ELT) 

•                     Unified and scalable data models 

•                     System Thinking & Architecture

Ability to: 

•            Design scalable backend architectures 

•            Define and manage data flow across systems 

•            Guide integration strategies between multiple services and platforms 

Good to Have

•            Experience with cloud platforms (AWS / GCP / Azure) 

•            Exposure to real-time data processing systems 

•            Knowledge of ML data pipelines or analytics workflows 

•            Experience using Python for data ingestion pipelines, transformation scripts, and automation workflows is an advantage

What We’re Looking For

•            Passion for experimenting with new tools, frameworks, and ideas 

•            Ability to quickly prototype and iterate on features 

•            Interest in blending creativity with engineering 

•            Comfort working in less rigid, exploratory development environments 

•            Strong curiosity and self-learning mindset 

Bonus Points If You

•            Use AI-assisted coding tools (like GitHub Copilot, Cursor, Antigravity, Claude) 

•            Build side projects, prototypes, or experimental apps 

•            Enjoy rapid MVP development, or creative coding 

•            Stay updated with latest trends in AI, and developer tools