Senior Data Engineer
5 hours ago
Senior Data Engineer Will design, build, and maintain large-scale, secure, and high-performance data pipelines to support critical Financial Services workloads. The role focuses on data modernization, regulatory data aggregation, and AI/ML enablement across domains such as Core Banking, Payments, Risk, Treasury, and Regulatory Reporting. The ideal candidate is an experienced data engineering professional with strong technical expertise in modern data platforms and a deep understanding of Financial Services data and regulatory frameworks. Job Responsibilities Design, implement, and optimize ETL/ELT data pipelines using Apache Spark, PySpark, Databricks, or Azure Synapse. Build and operationalize real-time streaming pipelines using Kafka, Confluent, or Azure Event Hubs for risk, liquidity, and transaction data. Integrate and transform data across multiple financial systems – Core Banking, Trade, Payments, Treasury, CRM, and Compliance. Implement robust data quality, validation, and lineage controls using Great Expectations, Deequ, or dbt tests. Develop and maintain data models and schemas (3NF, Dimensional, Data Vault 2.0). Ensure compliance with MAS TRM, PDPA, PCI-DSS, and other financial data security standards by implementing data encryption, masking, and tokenization. Collaborate with Data Scientists and AI Engineers to enable ML feature stores and model deployment pipelines. Support regulatory and risk data flows for MAS 610/649, Basel III/IV, and other regulatory requirements. Maintain CI/CD pipelines for data infrastructure using Azure DevOps, Terraform, and GitHub Actions. Job Requirements Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field. Preferred certifications: Snowflake SnowPro Core. 6-10 years of experience in data engineering, with at least 3 years in the BFSI domain. Proven experience in building real-time and batch data pipelines on Azure or AWS ecosystems. Strong proficiency in Python, PySpark, SQL, and Scala for data transformation, processing, and automation. Hands‑on experience with modern data platforms such as Azure Data Lake, Azure Synapse Analytics, Databricks, and Snowflake. Proven ability to design, schedule, and monitor data workflows using orchestration tools like Apache Airflow, Azure Data Factory, and dbt. Experience in developing and maintaining real‑time data streaming pipelines using Kafka, Confluent, or Azure Event Hubs. Familiarity with data governance frameworks and tools such as Apache Atlas, Azure Purview, and Collibra. Strong understanding of data security practices, including encryption, RBAC, tokenization, and audit logging, aligned with regulatory and compliance standards. Practical knowledge of CI/CD and Infrastructure‑as‑Code tools – Terraform, Azure DevOps, and GitHub Actions. Exposure to regulatory data models such as MAS 610, Basel III, IFRS 9/17, BCBS 239. Experience integrating DevOps and MLOps practices into data engineering workflows. Strong analytical and problem‑solving mindset with attention to detail. Ability to collaborate effectively with cross‑functional and geographically distributed teams. Excellent written and verbal communication skills. Demonstrated ownership, accountability, and commitment to quality and delivery. #J-18808-Ljbffr
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