Description

• SQL: Strong experience with data analysis, data transformation, querying, validation, and database development.
• Python: Strong experience in data engineering, analytics, automation, or data processing.
• Apache Spark: Strong experience with distributed data processing and large-scale data workloads.
• Database technologies: Experience with Oracle, MySQL, and SQL Server.
• Data warehousing: Strong understanding of data warehouse architecture, modeling, and implementation.
• Data lakes: Experience with data lake concepts, data ingestion, storage, processing, and governance.
• Big data platforms: Strong technical understanding of platforms supporting large-scale data processing and analytics.
• Data modeling: Experience designing conceptual, logical, and physical data models.
• Data integration: Experience designing and implementing data integration solutions.
• Data governance: Experience with data quality, standards, ownership, metadata, access, and lifecycle controls.
• Data visualization and BI: Knowledge of Tableau and Power BI.
• Cloud data solutions: Familiarity with AWS and Azure data environments.
• Project delivery tools: Ability to use tools for project planning, delivery schedules, documentation, collaboration, and reporting. 
  
   

Preferred

 

• Experience with cloud-native data architectures and data engineering services.
• Experience delivering data-driven solutions in complex enterprise environments.
• Experience with data quality automation, metadata management, lineage, and reconciliation.
• Experience with advanced analytics, reporting, or self-service BI solutions.
• Certification in data engineering, analytics, cloud, database, or BI technologies.
• Experience improving data-processing efficiency and optimizing cloud resource usage. 
  
   

Overall Responsibilities

 

• Lead and manage Data and Analytics projects from requirements definition through delivery and transition.
• Work closely with clients and internal stakeholders to understand business requirements and develop data-driven solutions.
• Provide technical expertise and guidance to junior team members and project teams.
• Oversee data collection, cleansing, transformation, validation, and preparation processes.
• Design and implement data architectures, data models, data warehouses, data lakes, and data integration solutions.
• Develop and maintain project plans, delivery schedules, milestones, dependencies, risks, and resource plans.
• Lead data analysis and modeling activities to address business and analytical requirements.
• Guide the selection and application of data technologies, tools, platforms, and delivery approaches.
• Ensure data solutions meet requirements for quality, scalability, maintainability, security, and performance.
• Lead technical discussions with clients, architects, analysts, engineers, and other stakeholders.
• Review technical outputs, data models, integration designs, analytical solutions, and project deliverables.
• Monitor project progress, quality, budget, risks, and delivery timelines.
• Mentor team members and support knowledge sharing, technical development, and consistent engineering practices.
• Ensure project deliverables are completed on time, within budget, and in accordance with agreed standards.
• Promote efficient use of data storage, processing, and cloud resources to support sustainable technology practices. 
  
   

Technical Skills (By Category)

Programming Languages

Essential

 

• Python.
• SQL.
• Apache Spark development and data-processing capabilities.
• Ability to write maintainable code for data transformation, validation, automation, and analytics.
• Experience applying programming and query techniques to large-scale data workloads. 
  
   

Preferred

 

• Experience with additional scripting or programming languages used for data engineering, automation, or analytics.
• Experience developing reusable data-processing frameworks, utilities, or automation components. 
  
   

Databases/Data Management

Essential

 

• Oracle.
• MySQL.
• SQL Server.
• Data warehousing.
• Data lakes.
• Big data platforms.
• Data modeling and data integration.
• Data collection, cleansing, preparation, transformation, and validation.
• Data governance and data quality management.
• Understanding of data structures, relationships, lineage, ownership, access, and lifecycle management. 
  
   

Preferred

 

• Experience with data migration, reconciliation, metadata management, lineage, and master data practices.
• Experience with database and data-platform performance optimization.
• Experience designing data solutions for high-volume or complex analytical workloads. 
  
   

Cloud Technologies

Essential

 

• Familiarity with cloud-based data solutions.
• AWS.
• Azure.
• Understanding of cloud data storage, processing, integration, scalability, availability, and governance considerations. 
  
   

Preferred

 

• Experience designing or implementing cloud-native data architectures.
• Experience with cloud data warehouses, data lakes, processing platforms, and integration services.
• Experience with cloud monitoring, cost management, resource optimization, and sustainable data-processing practices.
• Experience supporting cloud migration or data-platform modernization. 
  
   

Frameworks and Libraries

Essential

 

• Apache Spark.
• Frameworks and libraries used for Python-based data engineering and analytics.
• Components supporting data ingestion, transformation, validation, integration, and analytics.
• Tools supporting data visualization and business intelligence, including Tableau and Power BI. 
  
   

Preferred

 

• Libraries supporting advanced analytics, data quality, profiling, lineage, and workflow automation.
• Reusable data-engineering components and shared analytical frameworks.
• Tools supporting self-service reporting and interactive data exploration. 
  
   

Development Tools and Methodologies

Essential

 

• Data and Analytics project delivery.
• Project planning and delivery scheduling.
• Requirements analysis and solution design.
• Data architecture and modeling.
• Data integration and pipeline development.
• Data governance and quality practices.
• Client and stakeholder collaboration.
• Technical reviews and delivery reporting.
• Team leadership, mentoring, and project coordination.
• Ability to manage multiple tasks, priorities, dependencies, risks, and deliverables. 
  
   

Preferred

 

• Agile delivery methodologies.
• Project portfolio management and governance practices.
• Automated data-quality checks, testing, monitoring, and deployment.
• Experience with source control, CI/CD, workflow orchestration, and data pipeline monitoring.
• Experience establishing data engineering standards and delivery metrics. 
  
   

Security Protocols

Essential

 

• Apply data security and governance practices throughout data collection, storage, processing, integration, and reporting.
• Protect sensitive business and client data through appropriate access controls, permissions, and secure handling practices.
• Consider data privacy, retention, classification, lineage, and auditability requirements in solution design.
• Identify and escalate security, privacy, data-quality, and operational risks. 
  
   

Preferred

 

• Experience implementing security controls in cloud-based data platforms.
• Experience supporting regulatory, audit, privacy, or compliance requirements.
• Experience with data masking, encryption, role-based access, and secure data integration.
• Experience incorporating security and governance checks into data delivery processes. 
  
   

Experience Requirements

 

• Required: Minimum of 7 years of experience in Data and Analytics projects.
• Experience leading project teams and managing project delivery.
• Proven track record of delivering data-driven solutions to clients.
• Strong experience with Python, Apache Spark, SQL, data warehousing, data lakes, big data platforms, data modeling, and data integration.
• Experience with Oracle, MySQL, SQL Server, Tableau, Power BI, AWS, and Azure is required or preferred according to the specific project scope.
• Experience managing project plans, delivery schedules, technical discussions, quality, budgets, risks, and stakeholder expectations.
• Experience mentoring junior team members and providing technical guidance.
• Industry-specific experience is not mandatory; experience delivering data solutions in complex enterprise environments is preferred.
• Candidates may also qualify through an equivalent combination of relevant data engineering, data analytics, project leadership, client delivery, technical training, and demonstrated delivery outcomes. 
  
   

Day-to-Day Activities

 

• Lead data analysis, modeling, architecture, integration, and solution-design activities while working with clients to understand data needs.
• Manage project delivery through planning, scheduling, progress tracking, risk management, stakeholder coordination, and budget monitoring.
• Lead technical discussions with clients and internal teams and provide guidance on data platforms, models, governance, quality, and integration approaches.
• Mentor team members, review deliverables, ensure quality and timely delivery, and make technical decisions within agreed project standards. 
  
   

Qualifications

 

• A bachelor’s or master’s degree in Computer Science, Information Systems, Engineering, or a related field is required; equivalent relevant education and experience may be considered.
• Minimum of 7 years of experience in Data and Analytics projects, including project leadership and delivery management.
• Demonstrated experience with Python, Apache Spark, SQL, databases, data modeling, data integration, data warehousing, data lakes, and big data platforms.
• Certifications in data engineering, analytics, cloud platforms, databases, BI, or related technologies are preferred but not mandatory.
• Maintain continuous professional development in data engineering, analytics, cloud platforms, data governance, visualization, project delivery, and emerging data technologies

Key Skills

Education

Bachelor's or Master's degrees