Data Architect - Python/ETL (8-14 yrs)

Job Summary :

To design, implement, and optimize scalable data architectures to support complex financial data workflows,enabling analytics and insights in a highly dynamic environment with the experience of data architect role and hands-on expertise in Databricks and a strong understanding of fixed income instruments or capital markets.

Desired Skills and Experience :

Technical Expertise :

- Proven hands-on experience with Databricks and Apache Spark.

- Strong knowledge of cloud platforms (Azure, AWS, or GCP) and data lake architectures.

- Proficiency in programming languages like Python and SQL.

- Experience with database systems such as MongoDB, PostgreSQL, or SQL Server.

- Deeper understanding in Data governance.

Domain Knowledge :

- Strong understanding of fixed income instruments (e.g., bonds, credit default swaps) and capital market workflows.

- Familiarity with market data sources, trading systems, and financial reporting.

Education & Experience :

- Bachelor's or master's degree in computer science, Engineering, or a related field.

- 8+ years of experience in data architecture or engineering, with 3+ years of hands-on experience in Databricks.

- Previous experience in the financial services sector, particularly with capital markets or asset management.

Soft Skills :

- Excellent problem-solving and analytical skills.

- Strong communication and collaboration abilities to work across business and technical teams.

Preferred Qualifications :

- Experience with Delta Lake for transactional data processing.

- Familiarity with fixed income risk analytics and regulatory requirements.

- Certification in Databricks, cloud platforms, or related technologies.

Key Responsibilities :

Data Architecture & Design :

- Design and implement modern, scalable data architectures using Databricks.

- Define and maintain data models, pipelines, and governance strategies tailored to capital market datasets.

- Build robust ETL processes and optimize data workflows for performance and reliability.

Data Engineering & Integration :

- Develop and manage end-to-end data pipelines on Databricks, integrating diverse data sources (structured

and unstructured).

- Leverage tools like Spark, Delta Lake, and Python/Scala for data transformation and enrichment.

- Ensure compliance with data security and privacy regulations (e.g., GDPR, CCPA).

Domain-Specific Knowledge :

- Utilize expertise in fixed income instruments (e.g., bonds, TIPS, interest rate swaps) or capital markets to design data solutions aligned with business needs.

- Collaborate with quantitative analysts, portfolio managers, and traders to support analytics and decision-making.

Technology Leadership :

- Provide technical guidance to data engineering and analytics teams.

- Stay updated on the latest trends in Databricks and capital markets, driving innovation.

- Implement best practices for data architecture, quality assurance, and DevOps in a cloud environment.

Stakeholder Collaboration :

- Partner with business leaders, technology teams, and data scientists to align data strategies with

organizational objectives.

- Prepare documentation, architecture diagrams, and operational guidelines for cross-team collaboration