We are seeking an experienced, leadership-driven Big Data Specialist to lead our data engineering and analytics initiatives and maintain the highest standards of data ingestion, processing, and storage efficiency. In this senior role, you will oversee the design and architecture of our large-scale data ecosystems, enabling advanced analytics, machine learning models, and real-time data streaming. If you have strong team leadership skills, deep knowledge of distributed systems, and a passion for operational excellence and robust data infrastructure, this is an outstanding opportunity to shape the data-driven intelligence and competitive advantage of our organization.

Key Responsibilities
Team Leadership: Manage, mentor, motivate, and evaluate the data engineering team, including big data engineers, data architects, and ETL developers.

Standards & Quality Control: Establish and enforce high standards for data pipeline architecture, data quality, and schema design; conduct regular code reviews and performance audits of data workflows.

Operations Planning: Develop and manage data infrastructure roadmaps, migration strategies to modern data lakes/warehouses, and long-term scalability plans.

Resource & Budget Management: Control costs associated with cloud data services (e.g., AWS EMR, Google Dataproc, Snowflake), storage, and cluster resources; manage relationships with data infrastructure vendors.

Stakeholder Collaboration: Collaborate with Data Scientists, Business Analysts, and Executive leadership to align data architecture with strategic business goals and analytical requirements.

Training & Development: Design and deliver comprehensive training programs on big data technologies (e.g., Spark, Kafka, Hadoop), data modeling best practices, and CI/CD for data pipelines; mentor team leads.

Compliance & Reliability: Ensure full adherence to data privacy regulations (e.g., GDPR, CCPA), data governance frameworks, and security best practices regarding sensitive information.

Reporting & Continuous Improvement: Prepare reports on data platform performance, pipeline latency, and data utilization costs; implement innovative data engineering methodologies and automated pipeline monitoring.

Requirements & Qualifications
Education: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field is preferred. Relevant certifications in cloud data platforms (AWS, Azure, or GCP) are highly desirable.

Experience: 5–10+ years in big data engineering or architecture, with at least 3–5 years in a leadership, senior, or managerial capacity. Experience in architecting high-volume, enterprise-grade data ecosystems is highly desirable.

Technical & Management Skills:

Expertise in distributed computing frameworks (e.g., Apache Spark, Flink), message queues (e.g., Kafka), and data warehousing (e.g., Snowflake, BigQuery, Redshift).

Strong knowledge of SQL, NoSQL, data modeling, ETL/ELT pipelines, and Infrastructure-as-Code.

Proven ability to lead high-performing technical teams, manage complex data migrations, and optimize cost and performance for massive datasets.

Soft Skills:

Excellent leadership, communication, and interpersonal skills, with the ability to explain complex data architectures to non-technical stakeholders.

Strong problem-solving, decision-making, and organizational abilities.

High attention to detail with a focus on data integrity and pipeline reliability.

Ability to work under pressure and manage multiple high-stakes data projects in a dynamic environment.

Share

Comments