Advanced Data Systems Engineer

1 week ago

Kanpur, Uttar Pradesh, India beBeeData Full time ₹ 25,00,000 - ₹ 30,00,000

Job Description:

  • Project Leadership & Delivery:
    • Lead the end-to-end delivery of advanced data systems for mission-critical Tier 1 applications, focusing on revenue management and other high-impact use cases.
    • Ensure solutions meet NFRs (e.g. scalability, performance, availability, security) and SLAs, delivering on time, within scope, and budget.
    • Utilize the ArcOne AI platform to design, scale, and tune system topologies to support complex real-time and batch workloads.
  • Data Architecture Leadership:
    • Architect and oversee the implementation of robust, scalable, and secure data architectures to support mission-critical applications.
    • Design data models, schemas, and storage solutions (e.g. data lakes, data warehouses, NoSQL databases) optimized for performance, scalability, and accessibility.
    • Implement and optimize ETL/ELT pipelines, ensuring efficient data ingestion, transformation, and integration across distributed systems.
    • Ensure data governance, integrity, and security standards are maintained across all data architectures.
  • Team Management:
    • Manage, mentor, and inspire a team of data engineers, architects, and performance engineers, fostering a culture of technical excellence and collaboration.
    • Provide technical guidance on data architecture, advanced data systems, and system optimization, ensuring alignment with industry best practices.
    • Conduct performance reviews, set team goals, and support career development for team members.
  • Technical Expertise:
    • Oversight of high-scale, mission-critical systems handling real-time (e.g. streaming, event-driven) and batch processing workloads.
    • Leverage the ArcOne AI platform to design and scale system topologies, tuning components (e.g. compute, storage, network) to meet performance and scalability requirements.
    • Optimization of data processing frameworks (e.g. Apache Kafka, Spark, Hadoop, Flink) and databases (e.g. SQL, NoSQL) for low latency, high throughput, and reliability.
  • Stakeholder Collaboration:
    • Act as the primary point of contact for stakeholders, translating business requirements into technical solutions with a focus on robust data architectures.
    • Collaborate with infrastructure, platform, AI, and product teams to align on system design, performance, and data architecture goals.
    • Communicate project progress, risks, and performance metrics to senior leadership and clients.

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