Senior Data Analyst- ADF,Snowflake,SQL,ETL/Powe Center,Data Modeling

The Data Platform Lead is a senior technical role responsible for owning the design, development, and operational management of the enterprise data platform.

The incumbent will lead the end-to-end delivery of data integration pipelines using Azure Data Factory (ADF) and Informatica, manage the cloud data warehouse on Snowflake or Azure Synapse, and drive the enterprise data modeling strategy across all business domains.

In addition to the primary platform responsibilities, the role carries a secondary accountability for data stewardship — ensuring data quality standards, governance policies, and data definitions are established and upheld across the data platform in support of reliable and trustworthy data for the organisation.

Candidate must possess at least a Bachelor’s Degree in Computer Science, Information Technology, Information Systems, or a related field

Minimum 7 years of working experience in data management, with at least 3 years in a senior or lead capacity covering data platform engineering, data warehousing, or data integration

Hands-on experience with Azure Data Factory (ADF) — designing, building, scheduling, and monitoring data integration pipelines; experience with ADF components including Linked Services, Datasets, Data Flows, Triggers, Integration Runtimes, and pipeline parameterisation

Experience troubleshooting ADF pipeline failures, optimising data flow performance, and implementing error handling and retry logic within ADF pipelines

Hands-on experience with Informatica PowerCenter or Informatica Intelligent Cloud Services (IICS) — designing, developing, and maintaining ETL mappings, workflows, and sessions; ability to build, enhance, and troubleshoot Informatica packages in a production environment

Strong expertise in data warehouse design and management — including dimensional modeling (star/snowflake schema), data vault methodology, and enterprise warehouse operations in production environments

Hands-on experience with cloud data warehouse platforms such as Snowflake, Azure Synapse Analytics, or Azure SQL Data Warehouse, including performance tuning, partitioning strategies, and cost optimisation

Experience with Azure Analysis Services (AAS) for building and managing tabular data models and data cubes to support enterprise BI and self-service analytics

Proven experience in data modeling strategy — developing and maintaining conceptual, logical, and physical data models; proficiency in modeling tools such as ERwin, ER/Studio, or dbt

Strong proficiency in SQL — complex query writing, optimisation, and performance tuning across relational and columnar databases (MSSQL, Snowflake, PostgreSQL)

Familiarity with data governance principles and data stewardship practices — including data quality management, data lineage, metadata management, and data cataloguing (e.g.

Microsoft Purview, Collibra, or Alation)

Strong analytical, problem-solving, and conceptual thinking skills with the ability to translate complex business requirements into scalable data platform solutions

Good communication and stakeholder management skills; able to present data platform strategies and findings clearly to both technical and non-technical audiences

Exposure to Business Intelligence (BI) and reporting tools such as Power BI and SQL SSRS will be an added advantage

Experience with scripting languages such as Python or PowerShell for data automation and pipeline orchestration will be an added advantage

Familiarity with regulatory and compliance requirements relevant to data management (e.g.

GDPR, data residency policies) will be an added advantage

Design, build, and maintain ADF pipelines to support data ingestion, transformation, and distribution across source systems, the data warehouse, and downstream consumers

Configure and manage ADF components including Linked Services, Datasets, Mapping Data Flows, Copy Activities, Triggers, and Self-hosted Integration Runtimes to meet enterprise data integration requirements

Implement parameterised and reusable pipeline patterns to standardise data movement across projects, reducing duplication and improving maintainability

Monitor ADF pipeline runs, proactively detect and resolve failures, and implement robust error handling, alerting, and retry strategies to ensure pipeline reliability and SLA adherence

Optimise ADF Data Flow performance through partition tuning, compute scaling, and transformation logic review to reduce execution times and Azure compute costs

Govern ADF development standards — naming conventions, version control via Git integration, and deployment practices across DEV, SIT, UAT, and PROD environments