Data Engineering MTS/SMTS - ETL

4+ years of overall experience in developing, maintaining, and supporting ETL/ELT pipelines, enterprise integrations, data warehouse platforms, and production systems.

Primary Responsibilities

• Develop, enhance, and support data ingestion and ETL/ELT solutions using Python, Snowflake, SQL, Informatica, or BusinessObjects Data Integrator.
• Design, maintain, and monitor Apache Airflow DAGs for scheduling, workflow orchestration, dependency management, retries, and failure recovery.
• Develop and support MuleSoft APIs and integrations connecting Salesforce, enterprise applications, databases, and data platforms.
• Provide production and agentic support using AI-assisted monitoring, troubleshooting, knowledge retrieval, incident summarization, and resolution recommendations.
• Monitor scheduled jobs, APIs, interfaces, data availability, data quality, and upstream/downstream dependencies to meet operational SLAs.
• Perform incident triage, restore failed services, coordinate with stakeholders, and track Time to Resolution (TTR).
• Support month-end, quarter-end, and year-end activities by assisting with the SoX process in collaboration with Internal and External Auditors
• Knowledge of Finance and HCM systems is a significant advantage.
• Conduct Root Cause Analysis (RCA) for recurring or critical incidents. Document corrective and preventive actions and track them through closure.
• Support release management activities, including deployment planning, release validation, production verification, rollback readiness, and post-release monitoring.
• Create and maintain operational run lists, production schedules, release checklists, job inventories, dependency details, and escalation contacts.
• Develop and maintain Standard Operating Procedures (SOPs), runbooks, support documentation, knowledge articles, and RCA reports.
• Perform operational activities such as system patching, password and credential rotation, certificate renewal, access management, configuration changes, and environment migrations.
• Support Salesforce-related integrations with an understanding of Salesforce objects, APIs, authentication, batch interfaces, data flows, and integration troubleshooting.
• Perform data validation, source-to-target reconciliation, completeness checks, and failed-job recovery to ensure reliable data delivery.
• Collaborate with data engineering, Salesforce, application, infrastructure, security, database, and business teams to resolve issues and deliver stable solutions.

Required Skills

• Strong SQL development, data analysis, and production troubleshooting skills.
• Hands-on experience with Python and Snowflake.
• Experience with Apache Airflow workflow development, scheduling, and monitoring.
• Experience supporting MuleSoft APIs and enterprise integrations.
• Understanding of ETL/ELT processes, API integrations, job monitoring, incident management, release management, and SLA-based production support.
• Working knowledge of Salesforce platform concepts, objects, APIs, authentication, and integration patterns.
• Knowledge of data warehouse concepts, including fact and dimension tables, star and snowflake schemas, conformed dimensions, and Slowly Changing Dimensions.
• Ability to create clear SOPs, runbooks, RCAs, operational reports, and technical documentation.
• Strong communication, collaboration, analytical, and problem-solving skills.

Desired Skills

• Experience with Informatica or Data Integrator.
• Tableau reporting and dashboard-development experience.
• Familiarity with Salesforce data models and tools such as Bulk API, REST API, Platform Events, or Change Data Capture.
• Experience with AI-assisted or agentic support solutions for monitoring, incident analysis, automated remediation, and knowledge management.
• Knowledge of CI/CD pipelines, Git, automated deployment, release controls, and rollback procedures.
• Experience with cloud platforms, observability tools, service-management platforms, and enterprise alerting solutions.
• Familiarity with Agile delivery, ITIL processes, change management, and problem management.
• A demonstrated, genuine AI-first approach to engineering — using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
• Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows.
• Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.