Snowflake Data Engineer
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Summary
Data engineer who designs, builds, and operates scalable ETL/ELT pipelines, integrations, and data feeds on Snowflake and Azure for financial data, using Python, PySpark, SQL, and dbt. Hybrid role in Hyderabad (3 days in office); immediate joiners preferred.
Position- Snowflake Data Engineer
Experience-3+ Years
Location- Hyderabad ( Hybrid-3 Days)
Notice Period-Immediate
Mandatory Skills: Snowflake, DBT, Data Engineer, Azure, Python, Pyspark, SQL, Data Warehouseing
- 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, collaborative 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
