Key Responsibilities

- Data Platform Engineering: Design, build, test, and maintain scalable, reliable, and fault-tolerant ETL/ELT pipelines and reusable platform components across Azure and Snowflake.
- Data Ingestion & Source Onboarding: Integrate complex financial data from internal systems, third-party providers, databases, APIs, and files; support batch and near-real-time ingestion patterns where appropriate.
- Integrations, Data Feeds & MDS Support: Develop and operate inbound and outbound integrations and data feeds, support MDS tools and dependencies, troubleshoot production issues, and help ensure reliable delivery to downstream consumers.
- Data Reliability & Operations: Monitor pipeline health, freshness, volume, schema changes, and failures; respond to incidents, perform root-cause analysis, and improve performance, resilience, recoverability, and cost efficiency.
- Data Quality, Governance & Security: Implement automated validations, documentation, lineage, cataloging, access controls, encryption, and sensitive-data protections in accordance with financial-industry governance and security standards.
- Engineering Practices: Use Python, SQL, and fit-for-purpose data tools to deliver maintainable solutions; follow Git-based workflows, code reviews, testing, automated deployment, and CI/CD standards using platforms such as Azure DevOps or GitHub Actions.
- Business & Technical Collaboration: Partner with product managers, business users, operations teams, data stewards, analysts, and engineers to understand processes, clarify requirements, and translate business needs into scalable technical solutions.
- Global Delivery & Continuous Improvement: Participate actively in agile ceremonies, retrospectives, documentation, and knowledge sharing across the GCC and U.S. teams, and responsibly use AI-assisted engineering methods to improve development, testing,



documentation, and troubleshooting.

Knowledge, Skills, Abilities

- Practical experience designing, building, and supporting ETL/ELT pipelines, data ingestion processes, system integrations, and data feeds
- Working knowledge of Python and SQL for data movement, automation, validation, troubleshooting, and operational support
- Experience with cloud data platforms, especially Microsoft Azure and Snowflake
- Familiarity with modern data engineering, governance, and observability tools, including Snowflake, dbt, Fivetran, Monte Carlo, Immuta, and Atlan
- Understanding of orchestration, APIs, database connectivity, file-transfer patterns, scheduling, and dependency management
- Familiarity with medallion architecture and core data modeling concepts used to organize and manage data across platform layers
- Understanding of data quality, lineage, cataloging, access control, privacy, and secure handling of sensitive or regulated data
- Experience with version control, cooperative Git workflows, automated testing, and CI/CD practices
- Strong problem-solving skills and the ability to investigate ambiguous data, integration, platform, or tool issues methodically
- Excellent communication skills and the ability to collaborate across technical and business teams in a hybrid, globally distributed organization
- Experience in wealth management, asset management, financial technology, or a related financial-services domain is a plus

Qualifications & Experience

- 2-8 years of experience in data engineering, data integration, software engineering, platform engineering, or a related technical role
- Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Statistics, or a related technical field, or equivalent practical experience
- Experience working with sensitive or regulated financial data and following secure data-handling practices is required

📌 Data Engineer (Hyderabad)
🏢 Tech Smcsquared Gcc
📍 Hyderabad