Data Engineer

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