Description

Must have skills

Core Technical Skills

7–10 years of hands‑on experience in data engineering and development roles.
Strong hands-on experience with AWS and/or Azure and/or GCP data services such as BigQuery, Synapse, Redshift, Databricks, etc. Any 2 cloud experience is mandatory.
Solid expertise in data modeling, ETL/ELT pipeline development, data warehousing, and lakehouse architectures.
Strong Python development skills for data engineering, automation, orchestration, and solution prototyping.
Extensive experience working with SaaS-based data platforms (e.g., Databricks, Snowflake, managed analytics services).
Experience implementing and integrating Master Data Management (MDM) solutions such as Informatica, Reltio, Profisee, or equivalent tools.
Practical knowledge of data governance, security, access control, and compliance requirements in cloud environments.
Basic exposure to Agentic AI concepts, such as AI-driven automation, data orchestration, or intelligent workflow augmentation.

Ability to design, build, and optimize scalable and high-performance data pipelines.
Experience in query optimization, performance tuning, and cost optimization across cloud data platforms.
Hands-on experience supporting analytics, BI, and downstream consumption layers.

Strong verbal and written communication skills to effectively collaborate with architects, QA, DevOps, and business stakeholders.
Ability to explain complex technical concepts clearly to non-technical audiences.

Good to have skills

Familiarity with AI/ML integration into data platforms and analytics workflows.
Prior experience working in a consulting or professional services environment.

Key responsibiltes

Design, develop, and maintain robust, scalable, and secure data pipelines and platforms across AWS, Azure, and GCP.
Write high-quality, maintainable code in Python and SQL; implement ETL/ELT processes and data transformations.
Collaborate with solution architects to implement approved data architectures and provide development-level design inputs.
Support pre-sales efforts by contributing to technical inputs, building PoCs, demos, and solution prototypes when required.
Implement data quality checks, governance standards, security controls, and compliance requirements in developed solutions.
Work closely with cross-functional teams to understand requirements and translate them into effective technical implementations.
Guide and mentor junior developers, conduct code reviews, and promote best practices within the team.
Stay current with emerging cloud, data engineering, and AI technologies and recommend improvements to existing platforms.

Education Qulification

1. Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field.
 

Certification If Any

AWS Data Analytics / Solutions Architect
Azure Data Engineer Associate
GCP Professional Data Engineer
SnowPro Core Certification
Any two of the above
Databricks, Snowflake, or other cloud data platform certifications are a plus

Key Skills

Education

Bachelor's or Master's degrees