Senior Data Engineer

Design and build end-to-end data pipelines (ETL/ELT) for structured and unstructured data

Develop scalable data platforms handling terabytes of data with high reliability and low latency

Strong expertise in data modeling, warehousing, and lakehouse architectures

Own data quality, lineage, governance, and observability frameworks

Hands-on experience with Spark, Hadoop, Kafka, Flink (batch + real-time processing)

Build and optimize high-throughput, distributed data systems

Experience in streaming + event-driven architectures for large-scale financial data

Deep expertise in AWS / Azure / GCP (Data Lakes, Warehouses, Compute, Storage)

Tools: Databricks, Snowflake, Redshift, Synapse, BigQuery

Pipeline orchestration using Airflow, Prefect, or similar frameworks

Strong coding in Python, SQL, Scala

Focus on performance optimization, reliability, and production-grade systems

Experience with CI/CD, DevOps, and infrastructure-as-code

Ability to design architectures and make technology decisions at scale

Strong understanding of:

Trade lifecycle, transactions, risk, compliance, regulatory reporting

Customer 360, payments, lending, capital markets data and Wealth Management Business

GenAI / LLM integration (RAG, embeddings, vector stores)

Lead legacy-to-cloud data platform migrations

Drive data re-engineering, system decomposition, and modernization initiatives

Drive resilience, failover, and incident response frameworks

Work across engineering, data science, risk, compliance, and product teams