DATA ANALYST - DIGITAL ENGINEERING

Job Overview

Roles & Responsibility:

• Develop, maintain, troubleshoot, and optimize SQL and PL/SQL solutions.
• Create and maintain Oracle database objects and support Oracle tools such as TOAD and SQL Developer.
• Troubleshoot database and application issues.
• Support data integration across Oracle, Denodo, PostgreSQL, Microsoft Access, Excel, and file-based sources.
• Maintain Microsoft Access applications, including VBA-based solutions.
• Develop reports and dashboards using SAP BusinessObjects, Power BI, Tableau, or similar tools.
• Automate reporting processes, scheduled jobs, file transfers, and recurring operational activities.
• Validate data to ensure reporting accuracy and consistency.
• Collect, cleanse, transform, and analyze structured and unstructured data.
• Conduct exploratory, statistical, trend, segmentation, and hypothesis-testing analyses to generate business insights.
• Develop reusable analytical workflows using SQL and Python or R.
• Develop predictive models, KPIs, and dashboards to support business decisions.
• Apply data governance, privacy, security, data quality, and responsible AI requirements throughout the analytics lifecycle.
• Use AI and GenAI to enhance analysis, automate reporting, and improve processes while ensuring responsible use
• Collaborate with cross-functional teams and mentor junior analysts.

Desired Candidate Profile:

• Bachelor's degree in Computer Science, Information Technology, Data Analytics, Engineering, or a related field.
• Strong experience with SQL, Oracle databases, and PL/SQL development.
• Experience with Oracle tools such as TOAD, SQL Developer, or PL/SQL Developer.
• Hands-on experience supporting Microsoft Access applications and VBA.
• Strong Microsoft Excel and reporting skills.
• Experience with database connectivity technologies including ODBC/OLE DB.
• Proficiency in Python or R for data analysis, visualization, automation, and statistical modelling.
• Strong foundation in statistics and predictive analytics.
• Experience combining, cleansing, validating, and analyzing data from multiple systems.
• Strong analytical, troubleshooting, communication, and stakeholder-management skills.
• Knowledge of AI/GenAI concepts, output validation, data privacy, and responsible AI practices.
• Understanding of data governance, security, lineage, metadata, and documentation standards.
• Knowledge of data governance, privacy, security, data lineage, and responsible AI.

Preferred Qualifications:

Experience with one or more of the following:

• Denodo, PostgreSQL, ETL, or data-integration technologies
• Power BI, Tableau, or SAP BusinessObjects
• Git and collaborative development practices
• Cloud data platforms such as Azure, AWS, Snowflake, Databricks, or Spark
• Analytics engineering and workflow orchestration tools
• MLOps practices and model lifecycle management
• Engineering, manufacturing, quality, supply chain, or operations analytics

Key Competencies:

• Analytical thinking and problem solving
• Attention to detail and data quality
• Root cause analysis and troubleshooting
• Data storytelling and communication
• Business acumen and stakeholder engagement
• Cross-functional collaboration
• Documentation and knowledge sharing
• Commitment to data ethics and responsible AI

Competencies