Job Purpose
As a key member of the DTS team, you will primarily collaborate closely with a leading global hedge fund on data engagements. Partner
with data strategy and sourcing team on data requirements to design data pipelines and delivery structures.
Key Responsibilities
- Engage with vendors and technical teams to systematically ingest, evaluate, and create valuable data assets.
- Collaborate with core engineering team to create central capabilities to process, manage and distribute data assts at scale.
- Interpret business requirements and work with internal resources as well as application vendors.
- Design, develop, and maintain custom data engineering pipelines.
- Troubleshoot and resolve data-related issues.
- Configure and create data models and data quality rules to meet customer needs.
- Review and analyze data from multiple internal and external sources.
- Analyze existing Python code and identify areas for optimization.
- Write new optimized SQL queries or Python scripts to improve performance and reduce run time.
- Identify opportunities for efficiency and innovative approaches to completing the scope of work.
- Write clean, efficient, and well-documented code that adheres to best practices and Council IT coding standards.
- Deliver results under demanding timelines to real-world business problems.
- Work independently and multi-task effectively.
- Configure system settings and options and execute unit/integration testing.
- Apply robust data quality rules to systemically qualify data deliveries and guarantee the integrity of financial datasets.
- Engage with technical and non-technical clients as SME on data asset offerings.
- Good communication (verbal and written)
- Experience in managing client stakeholders
Key competencies
Essential Skills
- B.Tech/ M.Tech/ MCA with 4+ years of overall experience.
- Skilled in Python and SQL.
- Experience with data modeling, data warehousing, and building data pipelines.
- Experience working with FTP, API, S3 and other distribution channels to source data.
- Experience working with financial and/or alternative data products.
- Experience working with cloud native tools for data processing and distribution.
- Experience with Snowflake, Airflow, Databricks, and PySpark
Nice to Have
