Data Architect

About the Company
JMAN Group is the commercial data partner for private equity, trusted by leading investors to unlock value and drive smarter, data-driven decisions. Combining the strategic thinking of a management consultancy with the capabilities of a technology company, we help clients solve complex business challenges through data, analytics, and innovation.
Our expertise spans industries including private equity, pharmaceuticals, government departments, and retail. We blend deep commercial insight with cutting-edge capabilities across Data Engineering, Data Science, Full Stack Development, and Artificial Intelligence to deliver measurable business outcomes.
Founded in 2010, JMAN has grown into a global team of 600+ professionals across Chennai, London, and New York. Today, we are helping organizations shape the future of data-led value creation. Our people are at the heart of our success. We invest heavily in training, professional development, and career growth,empowering our teams to deliver exceptional work while contributing to our journey of becoming a globally recognized brand.
With nearly 80% of our business coming from long-term client partnerships, we take pride in building trusted relationships through collaboration, adaptability, and a commitment to delivering lasting impact.
Technical Specifications:
8+ years of experience in data platform build or any related field.
Familiarity and has worked with cloud-based data warehousing solutions (e.g., Fabric,Snowflake, Redshift, Databricks) and principles
Proficient in SQL and Apache Spark / Python programming languages.
Experience with cloud platforms like Azure.
Experience in data pipelines and ETL/ELT tools, including AWS Glue/Azure Data Factory/ Synapse/Matillion/DBT
Experience in implementing or working with data governance frameworks and practices to ensure data integrity and regulatory compliance.
Knowledge of data quality tools and practices.
Good to have skills include: Data visualization using Power BI, Tableau, or Looker, and familiarity with full-stack technologies.
Experience with containerization technologies (e.g., Docker, Kubernetes)
Experience with CI/CD pipelines and DevOps methodologies.
Excellent communication, collaboration, and problem-solving skills.
Qualifications
ETL or ELT: Azure Data Factory, Databricks, Synapse, dbt (any two – Mandatory).
Data Warehousing: Azure SQL Server/Redshift/Big Query/Databricks/Snowflake (Anyone - Mandatory).
Data Visualization: Looker, Power BI, Tableau (Basic understanding to support stakeholder queries).
Cloud: Azure (Mandatory), AWS or GCP (Good to have).
SQL and Scripting: Ability to read/debug SQL and Python scripts.
Monitoring: Azure Monitor, Log Analytics, Datadog, or equivalent tools.
Ticketing & Workflow Tools: Freshdesk, Jira, ServiceNow, or similar.
DevOps: Containerization technologies (e.g., Docker, Kubernetes), Git, CI/CD pipelines (Exposure preferred).
Responsibilities:
Design and implement data pipelines using ETL/ELT tools and techniques.
Configure and manage data storage solutions, including relational databases, data warehouses, and data lakes.
Develop and implement data quality checks and monitoring processes.
Automate data platform deployments and operations using scripting and DevOps tools (e.g., Git, CI/CD pipeline).  Ensuring compliance with data governance and security standards throughout the data platform development process.
Troubleshoot and resolve data platform issues promptly and effectively.
Collaborate with the Data Architect to understand data platform requirements and design specifications.
Assist with data modelling and optimization tasks.
Work with business stakeholders to translate their needs into technical solutions.  Document the data platform architecture, processes, and best practices.
Stay up to date with the latest trends and technologies in full stack development, data engineering, and DevOps.
Proactively suggest improvements and innovations for the data platform.

Work arrangement
No