Position Overview :

We are looking for an exceptional Senior Data Science Engineer skilled at deriving deep insights from large-scale datasets and enabling data-driven decisions across the organization.

You will uncover patterns, detect anomalies, and identify inefficiencies within complex systems. With strong AWS expertise - and ideally, exposure to Databricks - you will help optimize our data infrastructure, enhance analytical workflows, and propose scalable architectural improvements.

Responsibilities :

- Analyze complex, large-scale datasets to uncover trends, patterns, anomalies, and strategic insights.

- Design, build, and optimize data pipelines, ETL/ELT flows, and analytical models using AWS services.

- Identify inefficiencies, bottlenecks, and data quality issues; recommend scalable, long-term solutions.

- Implement and tune distributed data processing workflows (Spark, EMR, Glue, or Databricks).

- Leverage analytics and statistical techniques to build models, derive insights, and support predictive capabilities.

- Collaborate with engineering, product, and business teams to define and deliver data-driven solutions.

- Perform root-cause analyses on data pipeline failures and optimize infrastructure for reliability and cost-effectiveness.

- Develop dashboards, tools, or automation that improve visibility and decision-making.

Requirements :

- 5 - 10 years of experience in data analysis, engineering, or related data-driven disciplines.

- Strong hands-on expertise across the AWS ecosystem, with a solid understanding of data architecture and cloud primitives.

- Highly proficient in Python and capable of building scalable data pipelines, transformations, and analysis workflows.

- 3 - 5 years of experience in data science or analytics, including statistical analysis and applied ML techniques.

- Understanding of system design, distributed architectures, and performance optimization.

- Exposure to Databricks is a strong advantage.

Nice To Have :

- Advanced degree in Computer Science, Data Science, Engineering, Statistics, or related field.

- Hands-on experience with distributed computing frameworks such as Spark, EMR, Glue, or Databricks.

- Strong SQL skills with experience in query tuning and performance optimization.

- Familiarity with AWS cost-optimization strategies and cloud monitoring tools.

- Understanding of data orchestration frameworks (Airflow, Step Functions) and event-driven architectures.

- Experience deploying ML models at scale or working with ML Ops toolchains.

- Knowledge of containerization (Docker, ECS, EKS) or serverless technologies.