Head - Data Platform

Key Responsibilities :

1. Technical Strategy and Leadership :

- Architectural Vision : Drive the overall technical strategy and roadmap for our software and data engineering platforms, ensuring alignment with long-term business goals.

- Leadership & Mentorship : Provide technical leadership, guidance, and mentorship to engineering teams, fostering a culture of technical excellence, accountability, and innovation.

- Strategy Alignment : Proven ability to translate business objectives into actionable technical strategy and execution plans.

- Stakeholder Management : Utilize strong communication and stakeholder management skills to influence technical decisions across organizational boundaries.

2. Architecture and Design Governance :

- HLD/LLD Expertise : Lead the development of high-level design (HLD) and detailed low-level design (LLD) documents for complex engineering solutions.

- Design Patterns : Possess a strong foundation and expertise in applying design patterns, preferably using frameworks like Spring Boot (for Java-based systems) or Google Guice.

- System Resilience : Define and enforce architectural standards that prioritize scalability, performance, security, and resilience across all systems.

3. Data and Backend Engineering Execution :

- Big Data Technologies : Provide expert implementation and oversight using big data technologies such as Spark, Hadoop, Kafka, and other distributed computing frameworks.

- Data Processing : Demonstrate proficiency in SQL, Python, and Scala for complex data processing, transformation, and analytical workloads.

- Data Warehousing : Hands-on experience designing and managing data warehousing solutions using platforms like Snowflake, Redshift, or BigQuery.

- Backend Development : Expertise in backend development using a variety of programming languages, including Java, PHP, Python, Node.JS, and GoLang.

- NoSQL Databases : Experience with NoSQL databases such as Redis, Cassandra, MongoDB, and TiDB for specific data storage and caching needs.

4. Cloud, DevOps, and Compliance :

- Cloud Platforms : Strong understanding and practical experience with cloud platforms (AWS, GCP, or Azure) and their respective data services (e.g., S3/GCS, EMR/DataProc, RDS/Cloud SQL).

- DevOps Automation : Familiarity and hands-on experience with automation and DevOps tools and practices, including Jenkins,

Ansible, Docker, Kubernetes, Chef, Grafana, and the ELK stack (Elasticsearch, Logstash, Kibana).

- Data Governance & Security : Deep knowledge and practical experience in implementing solutions that adhere to stringent data governance, security, and compliance standards (e.g., GDPR, SOC2).

Required Technical Skills :

Backend & Core Languages :

- Java (Spring Boot preferred), Python, Node.JS, GoLang, PHP, JavaScript, HTML, CSS

Architecture & Design :

- HLD, LLD, Design Patterns, Spring Boot, Google Guice

Cloud Platforms :

- AWS, GCP, or Azure (strong understanding of data services)

Big Data & Processing :

- Spark, Hadoop, Kafka, Python, Scala, Advanced SQL

Data Stores :

- Data Warehousing : Snowflake, Redshift, BigQuery

- NoSQL : Redis, Cassandra, MongoDB, TiDB

DevOps & Monitoring :

- Jenkins, Ansible, Docker, Kubernetes, Chef, Grafana, ELK (Elasticsearch, Logstash, Kibana)

Governance & Security :

- GDPR, SOC2, Data Security, Data Governance Frameworks

Qualifications and Experience :

- Minimum 12+ years of progressive experience in software or data engineering.

- Minimum 3+ years in a technical leadership role (Lead Engineer, Architect, or Manager).

- Bachelor's or Master's degree in Computer Science, Data Engineering, or a related quantitative field.