We are unable to sponsor or take over sponsorship of an employment visa at this time.Job Title: Senior Data EngineerRole SummaryWe 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.
Key Responsibilities- 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.
Required Technical Skills- 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.
