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.