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  1. Data & Analytics Manager

  2. Senior Data Engineer

Data & Analytics Manager

Key Responsibilities

• Develop and execute a comprehensive data and analytics strategy that supports the organization's strategic objectives.

• Establish and maintain enterprise-wide data governance frameworks, policies, standards, and best practices.

• Transform business challenges into actionable, data-driven insights that support informed decision-making.

• Encourage the adoption of self-service analytics tools and capabilities across business departments.

• Oversee the architecture, implementation, and maintenance of enterprise data platforms, databases, data warehouses, and data lakes.

• Ensure seamless integration of data from internal systems and external sources while maintaining consistency and reliability.

• Optimize the scalability, availability, and performance of data infrastructure to support growing business needs.

• Ensure compliance with applicable data privacy regulations, internal governance policies, and security standards.

• Implement safeguards to protect confidential and sensitive organizational data.

• Identify, assess, and mitigate risks associated with data management, analytics processes, and information security.

• Partner with business units to identify analytical priorities and deliver solutions that address operational needs.

• Present technical findings in a clear and meaningful manner for executives and non-technical stakeholders.

• Lead, mentor, and develop teams comprising data analysts, data scientists, and data engineers.

• Establish team objectives, monitor performance, and ensure the timely delivery of high-quality analytics solutions.

• Foster a culture of continuous improvement by promoting analytics best practices and enhancing organizational data capabilities.

• Manage relationships with external vendors and technology partners where applicable.

• Collaborate with business stakeholders to integrate analytical insights into operational decision-making.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, or a related discipline.

• A minimum of seven years of professional experience in data analytics, data science, data engineering, or a closely related field.

• Demonstrated experience leading technical teams and managing data-focused projects.

Senior Data Engineer

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows to support enterprise reporting and analytics.

• Utilize technologies such as Apache Airflow, dbt, Kafka, Spark, and cloud-native data integration tools, including AWS Glue, Azure Data Factory, or Google Cloud Dataflow, to build reliable and resilient data solutions.

• Design and manage data models, data warehouses, and modern data storage architectures that support business intelligence and analytics.

• Develop and maintain cloud-based data warehouse platforms such as Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse while applying dimensional modeling and Data Vault methodologies.

• Maintain high standards of data quality by implementing validation rules, monitoring mechanisms, and anomaly detection processes.

• Support enterprise data governance initiatives by maintaining metadata, documenting data lineage, and ensuring compliance with regulatory requirements.

• Work closely with data scientists, analysts, and business stakeholders to understand business requirements and deliver effective data solutions.

• Design data architectures that promote collaboration, reduce data silos, and enable self-service analytics across the organization.

• Continuously monitor data pipeline performance and system health using observability platforms such as Datadog, Prometheus, and OpenTelemetry.

• Investigate and resolve production issues promptly to ensure uninterrupted data availability and system reliability.

• Implement robust data security measures, access controls, and compliance frameworks to safeguard organizational information.

• Promote engineering best practices, including version control, continuous integration and continuous deployment (CI/CD), infrastructure automation, and DevOps methodologies using technologies such as Git, Terraform, and Kubernetes.

• Evaluate emerging technologies and modern data engineering frameworks to drive innovation and improve platform performance.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field.

• At least four years of professional experience in data engineering or a similar technical role.

• Advanced knowledge of SQL, database design, and data modeling principles.

• Strong experience developing and managing ETL/ELT workflows using modern orchestration tools.

• Practical experience working with big data technologies and distributed data processing frameworks.

• Proficiency in programming languages such as Python, Scala, or Java.

• Hands-on experience building and managing cloud-based data engineering solutions on AWS, Microsoft Azure, or Google Cloud Platform (GCP).

• Good understanding of data warehouse, data lake, and lakehouse architectures.

• Familiarity with DevOps practices, CI/CD pipelines, and automation tools for data infrastructure.

• Strong knowledge of data governance, security, privacy, and regulatory compliance requirements.

• Demonstrated ability to design scalable, high-performance data pipelines capable of handling large and complex datasets.

• Ability to translate business requirements into practical and efficient data engineering solutions.

• Excellent communication and collaboration skills with experience working alongside business users, analysts, and data science teams.

Method of Application

Use the link(s) below to apply on company website.

• Data & Analytics Manager

• Senior Data Engineer