Big Data and SQL Developer

## For additional information, please review .

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• Technical Guidance :
• Provide technical leadership and guidance to multiple engineering teams, ensuring alignment with architectural principles and strategic goals.
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• Mentorship :
• Mentor senior and junior engineers, fostering their technical growth, problem-solving skills, and adherence to best practices.
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• Code Review & Quality Assurance :
• Lead comprehensive code reviews, ensuring high standards of code quality, efficiency, and maintainability.
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• Strategic Input :
• Contribute significantly to the Big Data strategy and roadmap, influencing technical decisions and long-term planning.
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• Knowledge Sharing :
• Drive initiatives for knowledge sharing, technical documentation, and community building within the Big Data engineering group.
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• Recruitment Support :
• Participate in interviewing and evaluating senior technical candidates.
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• Client & Business Partner Liaison :
• Act as a key technical liaison between the Big Data engineering team and internal business units, product managers, and clients.
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• Requirements Gathering :
• Proactively engage with stakeholders to understand business requirements, pain points, and opportunities, translating them into clear, actionable technical specifications and data solutions.
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• Technical Consultation :
• Provide expert technical consultation and guidance to clients and business partners on Big Data capabilities, limitations, and potential solutions.
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• Query Resolution :
• Assist clients and internal stakeholders with complex data queries, troubleshooting data discrepancies, and providing insights derived from Big Data platforms.
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• Communication :
• Effectively communicate complex technical concepts, project progress, risks, and solutions to non-technical audiences, ensuring transparency and alignment.
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• Cross-Functional Collaboration :
• Collaborate seamlessly with other teams (e.g., architecture, data sourcing) to ensure end-to-end delivery and support of data-driven initiatives.
• ## Exceptional problem-solving and analytical skills, with the ability to tackle highly complex technical and business challenges.
• ## Strong architectural thinking and the ability to design solutions that are scalable, maintainable, and resilient.
• ## Excellent verbal and written communication skills, with a proven ability to tailor technical explanations to diverse audiences, from engineers to senior business stakeholders and external clients.
• ## Demonstrated ability to build consensus, manage expectations, and influence outcomes with various stakeholders.
• ## Experience in mentoring and coaching other engineers.
• ## Proactive, self-motivated, and able to work effectively in a fast-paced, agile environment, often juggling multiple priorities.
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• 6+ years
• of progressive experience in software engineering, specifically focused on designing, building, and optimizing large-scale Big Data systems.
• ## Proven track record as a lead or principal engineer on significant data initiatives, demonstrating ability to drive technical projects from conception to deployment.
• ## Significant experience in client-facing or business partner-facing roles, with a proven ability to manage expectations, resolve issues, and build strong relationships.
• ## Experience in the financial services domain – Liquidity Management, Liquidity Stress Modelling, should be well verse with Markets terminologies. Also, experience with large data volumes, real-time processing, and stringent data quality / security requirements.
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• Core Big Data Frameworks :
• Expert-level proficiency with Apache Spark (Spark SQL, Spark Streaming, PySpark / Scala Spark), Hadoop ecosystem (HDFS, YARN).
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• Programming Languages :
• Expert in Scala, Python, or Java, with experience in developing robust, high-performance data processing applications.
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• Data Warehousing / Lakes :
• Deep understanding and practical experience with data warehouse models, and relevant technologies (e.g., Delta Lake, Iceberg, Parquet).
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• Databases :
• Strong proficiency in SQL; experience with NoSQL databases (e.g., Cassandra, MongoDB) is a plus.
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• DevOps & MLOps :
• Experience with CI / CD pipelines (e.g., Jenkins, GitLab CI), infrastructure as code (e.g., Terraform), containerization (Docker, Kubernetes / OpenShift), and monitoring tools. Familiarity with MLOps principles for data science workflows.
• Data Governance & Security :
• Solid understanding of data governance, data quality, data lineage, and data security principles in a Big Data environment.
• ## Bachelor’s degree / University degree or equivalent experience
• ## Master’s degree preferred

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