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Bravo Linkedin · Posted 6d ago

Senior Data Engineer

Qesm El Maadi

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Indexed description

Senior Data Engineer will own the end-to-end data architecture. You will build and scale high-throughput, low-latency pipelines that handle sensitive financial transactions, fraud detection, risk modeling, and regulatory compliance data. In a startup environment, this means wearing multiple hats: acting as a system architect, hands-on builder, and key collaborator with product, risk, analytics, and executive teams.

Responsibilities

Architecture & Scalability: Design, build, and optimize scalable batch and real-time streaming data pipelines (ETL/ELT) to process millions of financial events.

  • Financial Data Infrastructure: Build secure data lake houses and warehouses for transactions, ledger entries, risk metrics, and customer interaction data.

Compliance & Governance: Implement strict data security, encryption (at rest and in transit), PII masking, and access controls to comply with SOC2, PCI-DSS, GDPR, and financial regulatory standards.

Data Quality & Observability: Build automated testing, reconciliation frameworks, and real-time monitoring to guarantee zero-data-loss and auditability.

Cross-Functional Ownership: Partner with product managers, quantitative risk analysts, machine learning engineers, and finance teams to power real-time fraud engines, credit scoring models, and executive dashboards.

Qualifications & Requirements

3 years in software or data engineering with a track record of handling high-volume production data.

Strong Fintech / High-Growth DNA: You thrive in fast-moving, high-autonomy environments where you ship clean, production-grade code.

Proven Stack Mastery:

  • Languages: Advanced Python and/or Scala, plus expert SQL.
  • Streaming & Processing: Hands-on experience with Kafka, Spark, or Flink.
  • Warehousing & Orchestration: Snowflake, Databricks, or BigQuery + Airflow or Dagster.
  • Infrastructure as Code: Docker, Kubernetes, Terraform, AWS or GCP.

Precision Mindset: Deep appreciation for financial data accuracy, idempotent pipelines, and strict schema management.
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