Data Engineer

We are looking for a Senior Data Engineer to design, build, and optimize scalable data pipelines and data platforms supporting analytics, reporting, and AI/ML use cases. The ideal candidate has strong hands-on experience with Snowflake on AWS, Python-based ETL/ELT development, and enterprise scheduling/orchestration tools like Control-M, along with legacy/enterprise ETL experience in IBM DataStage. You will collaborate across engineering, analytics, and business teams in an Agile delivery model., * Design, develop, and maintain end-to-end data pipelines (batch and near real-time) using Snowflake, AWS services, and Python.

• Build and optimize data models in Snowflake (e.g., dimensional modeling, data vault, or curated data marts) for analytics and downstream consumption.
• Develop and maintain ETL/ELT workflows using Python and IBM DataStage; migrate/modernize workloads where applicable.
• Implement job scheduling, monitoring, and operational support using Control-M (alerting, retries, SLAs, and dependency management).
• Ensure data quality, governance, lineage, and documentation standards are met across pipelines.
• Perform performance tuning and cost optimization across Snowflake and AWS (query optimization, clustering, warehouse sizing, storage management).
• Partner with stakeholders (Data Science/AI, BI, Product, and Platform teams) to enable data products and AI-ready datasets.
• Participate in Agile ceremonies, contribute to estimation, planning, and sprint execution; follow SDLC and change management processes.
• Troubleshoot production issues, perform root-cause analysis, and drive preventative improvements.

• Snowflake: Strong expertise in Snowflake architecture, SQL development, performance tuning, security/roles, data loading/unloading, and best practices.
• AWS: Hands-on experience with AWS data ecosystem (commonly S3, IAM, CloudWatch; plus services such as Glue, Lambda, EC2, Step Functions, EMR, or Kinesis as applicable).
• Python: Strong Python programming for data engineering (ETL/ELT frameworks, API ingestion, automation, unit testing, logging).
• Control-M: Experience designing and managing enterprise job scheduling, dependencies, calendars, SLAs, monitoring, and incident handling.
• IBM DataStage: Solid experience building and maintaining DataStage jobs, handling complex transformations, and supporting production workloads.
• SQL: Advanced SQL skills for transformations, optimization, and data validation across large datasets.
• CI/CD & Version Control: Experience with Git and CI/CD practices for data pipelines (tools may vary).
• Operational Excellence: Monitoring, alerting, and production support experience in a 24x7 or business-critical environment.

Good to Have

• AI/ML exposure: Experience enabling AI/ML pipelines or feature datasets; familiarity with ML lifecycle concepts, feature engineering, or MLOps tools/processes.
• Experience with data governance/metadata tools and practices (catalog, lineage, data quality frameworks).
• Exposure to streaming or event-driven architectures.

Required Soft Skills

• Strong experience working in Agile/Scrum teams and delivering within structured SDLC processes.
• Excellent communication skills (technical and non-technical) with the ability to explain complex data concepts clearly.
• Proven ability to coordinate across multiple teams (Data Engineering, Data Science, DevOps, Security, BI, and business stakeholders).
• Strong ownership mindset, problem-solving ability, and attention to detail.

Qualifications (Typical)

• Bachelor s degree in Computer Science, Engineering, or related field (or equivalent practical experience).
• 9+ years of data engineering experience, including enterprise-grade data platform delivery and production support.