Senior Data Engineer – Snowflake / AWS / Python / DataStage

• Our client, an IT Services and Consulting company, is looking for a Senior Data Engineer – Snowflake / AWS / Python / DataStage for their Detroit, MI/Hybrid location.
• Design, build, and optimize scalable data pipelines and data platforms supporting analytics, reporting, and AIML use cases.
• 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 modelling, data vault, or curated data marts for analytics and downstream consumption.
• Develop and maintain ETLELT 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 Already 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.
• You will collaborate across engineering, analytics, and business teams in an Agile delivery model.
• Experience: 6 to 10 Yrs
• Primary Skills: Snowflake, SQL, AWS, Python, DBT & DataStage
• Secondary Skills: AWS Glue, Qlik, Control-M & Airflow.
• Required Technical Skills Snowflake Strong expertise in Snowflake architecture, SQL development, performance tuning, security roles, data loading unloading, and best practices.
• AWS Handson 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 ETLELT 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.
• CICD & Version Control Experience with Git and CICD 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 AIML exposure Experience enabling AIML pipelines or feature datasets familiarity with ML lifecycle concepts, feature engineering, or ML Ops tools processes.

None flagged.