Data Engineer with Capital Market exp
Description
Job Description
Data Engineer with Capital Market Experience
Roles & Responsibilities
- Understanding of post-trade data flows including allocations, confirmations, settlement, risk, finance, and regulatory reporting.
- Knowledge of securities data modeling and financial data structures. Experience with reference data, security master data, and transactional trade data.
- Understanding of time-series data management and financial market data is a strong plus.
- Strong SQL development skills with experience designing and optimizing relational data models and database schemas.
- Proficiency in Python, including data processing frameworks such as Pandas and PySpark.
- Strong understanding of data warehouse, data lake, and Data Lakehouse concepts.
- Experience working with large-scale transactional and analytical data platforms.
- Experience with PostgreSQL, SQL Server, Snowflake, Redshift, Databricks, or similar database and data warehouse technologies on cloud, preferably AWS.
- Experience with stream and batch data processing technologies such as Kafka, Spark, Flink, or similar platforms.
- Experience building batch and streaming data pipelines, including ETL/ELT workflows, using Python and SQL.
- Strong understanding of data quality, data lineage, data validation, and reconciliation processes.
- Experience working with messaging technologies such as Kafka, RabbitMQ, IBM MQ, or similar platforms.
- Familiarity with monitoring and observability tools such as Datadog, Prometheus, and Grafana.
- Understanding of data security, privacy, and regulatory compliance requirements.
- Exposure to data science, machine learning, and AI/GenAI data preparation concepts is a plus.
Skills
- AI
- AWS
- Cloud
- Data Lake
- Data Lineage
- Data Modeling
- Data Pipelines
- Data Quality
- Data Science
- Data Warehousing
- Databricks
- Datadog
- ELT
- ETL
- Flink
- Generative AI
- Grafana
- IBM MQ
- Kafka
- Lakehouse
- Machine Learning
- Observability
- pandas
- PostgreSQL
- Prometheus
- PySpark
- Python
- RabbitMQ
- Redshift
- Regulatory Compliance
- Snowflake
- Spark
- SQL
- SQL Server
- Time Series