Sr. Data Engineer

Job Summary: We are looking for a Senior Data Engineer to design, build, and optimize scalable data pipelines and data platforms supporting analytics, reporting, and AIML use cases. The ideal candidate has strong hand son experience with Snowflake on AWS, Python based ETLELT 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. Experience: 6 to 10 Yrs Primary Skills: Snowflake, SQL, AWS, Python, DBT & DataStage Secondary Skills: AWS Glue, Qlik, Control-M & Airflow., * 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.

• 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.
• Experience with data governance metadata tools and practices catalogue, lineage, data quality frameworks.
• Exposure to streaming or event driven architectures.

Qualifications: Typical Bachelor’s degree in Computer Science, Engineering, or related field or equivalent practical experience. 9 plus years of data engineering experience, including enterprise grade data platform delivery and production support.