Data Office Developer (SQL, Python, Data Engineering, Data Warehousing)

Overview

We are seeking a skilled Data Office Developer to design, implement, and support data solutions using SQL, Python, and data warehousing technologies. This role involves working closely with business and technical teams to address complex data processing and reporting requirements.

Key Responsibilities

• Create and manage database objects, queries, scripts, and data pipelines using SQL and Python.
• Analyze business and technical needs to develop scalable data solutions aligned with objectives.
• Handle data extraction, transformation, loading (ETL), validation, reconciliation, and reporting across diverse data platforms.
• Optimize SQL queries, stored procedures, and scripts to improve data analysis, reporting, and daily operations.
• Utilize relational and cloud-based databases such as Oracle, IQ, BigQuery, and HANA as relevant.
• Collaborate with business analysts, data architects, quality engineering, and stakeholders to understand data requirements and troubleshoot issues.
• Participate in data modeling activities including source-to-target data mapping, relationship identification, and understanding business rules and data lineage.
• Support dashboard and report development or validation leveraging BI tools like Tableau and Looker.
• Conduct data quality evaluations, analyze data problems, and support root cause investigations for defects or live issues.
• Develop and maintain comprehensive technical documentation including data mappings, design specifications, deployment instructions, and support handover guides.

Required Skills and Experience

• Extensive hands-on expertise in SQL including authoring, debugging, and tuning complex queries.
• Proficiency in Python for data manipulation, automation, scripting, and analytical tasks.
• Strong foundational knowledge of database concepts such as tables, views, joins, indexes, keys, normalization, stored procedures, and data integrity.
• Competency in analyzing data discrepancies, troubleshooting issues, and verifying results across source and destination systems.
• Experience handling large datasets and applying quality assurance, reconciliation, and validation methods.
• Effective communication skills to interact with both technical teams and business users.

Additional Skills (Desirable)

• Familiarity with database systems like Oracle, IQ, Google BigQuery, and SAP HANA.
• Knowledge of ETL process principles including extraction, transformation, load workflows, monitoring, scheduling, and error handling.
• Understanding of data modeling frameworks such as conceptual, logical, and physical models including star schemas, snowflake schemas, facts, dimensions, and source-to-target mapping.
• Experience with reporting and visualization software such as Tableau and Looker.
• Exposure to data warehousing, cloud analytics platforms, and enterprise reporting environments.