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Data Engineer Team Lead

amarbank · 30+ days ago
Negotiable
Full-time
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Responsibilities

  • Data Architecture and Engineering:
  • Maintaining existing design and ensuring the reliability of entire company pipelines on track with SLA. It is possible to redesign the architecture based on the results of the evaluation.
  • Optimize data storage solutions, including BigQuery, Dataflow, and other cloud-native services, to ensure efficient and secure handling of large datasets.

  • Business Collaboration and Strategy:
  • Work closely with business stakeholders to gather requirements, translate business needs into technical solutions, and ensure that data infrastructure supports key business objectives.
  • Partner with product teams, marketing, finance, and other departments to drive data-driven decision-making across the bank.

  • Data Governance and Security:
  • Ensure the data infrastructure complies with regulatory requirements and internal security standards, with a particular focus on data integrity, privacy, and governance.
  • Establish best practices for data quality, consistency, and accessibility across the organization.

  • Technical Leadership and Mentorship:
  • Provide guidance to the data engineering team, ensuring adherence to best practices in cloud architecture, data modeling, and pipeline development.
  • Mentor junior engineers and foster a culture of continuous learning and innovation within the team.

  • Performance Optimization and Innovation:
  • Continuously monitor and improve data processing performance, scalability, and cost-effectiveness.
  • Stay abreast of emerging trends and technologies in data engineering, recommending tools and practices that align with the bank's evolving needs.

  • Documentation and Reporting:
  • Maintain detailed documentation of data pipelines, workflows, and architecture to ensure transparency and audit-readiness.
  • Produce regular reports on data platform performance and provide insights on how data solutions are driving business outcomes.

Requirement

  • Bachelor’s degree in Computer Science, Information Technology, Data Science, or a related field.
  • Having 5-8 years of experience in data engineering, with a proven track record of leading data engineering projects on Google Cloud.
  • At least 2 years of experience in team management is required.
  • Strong technical expertise in Google Cloud services (BigQuery, Dataflow, Pub/Sub, etc.) and data processing frameworks.
  • Experience in data modeling, ETL/ELT processes, and building real-time and batch data pipelines.
  • Proven experience in ML-Ops.
  • Demonstrated ability to work with business stakeholders, translating technical language into business insights and ensuring data solutions support business needs.
  • Knowledge of the banking or financial services industry is highly desirable.
  • Proficiency in SQL, Python, or other relevant programming languages.
  • Strong understanding of data governance, privacy standards (e.g., GDPR), and compliance frameworks.
  • Excellent communication skills and ability to collaborate effectively across departments.

Bonus point

  • Proficiency in DPT Tools and Dataflex

Last updated on Sep 24, 2024

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