Job Description
KEY RESPONSIBILITIES :
– Define and drive the future strategy for GenAI and emerging technologies to uncover hidden patterns and drive decision-making across diagnostic products.
– Design, implement, and optimize data architectures using AWS services, ensuring seamless integration and high performance.
– Drive the design and development of efficient data processing workflows using PySpark, SparkSQL, SQL, and modern formats like Iceberg and Parquet.
– Optimize the performance, scalability, and cost efficiency of data infrastructure and AWS service consumption.
– Provide technical direction and mentorship to a team of developers, enforcing code quality standards and best practices for testing and CI/CD.
– Collaborate closely with engineering and business stakeholders to translate complex requirements into impactful data-driven solutions.
– Advocate for data security, governance, and compliance, ensuring all products are compliant with HIPPA, GDPR etc.
REQUIRED EXPERIENCE, SKILLS & QUALIFICATIONS :
– Minimum 8 to 15 years of hands-on data engineering experience, including significant leadership in technical projects.
– Expert-level proficiency in AWS services including S3, Redshift, Glue, Athena, EMR, Step Functions, and MWAA.
– Mastery of Python, SQL, and data processing frameworks such as Apache Spark.
– Proven experience with Dimensional Modeling, Data Partitioning, and Infrastructure as Code using Terraform.
– Demonstrated ability to provide technical direction, guide architectural decisions, and mentor cross-functional teams.
– Excellent skills in bridging the gap between business needs and technical implementation for both technical and non-technical audiences.
DESIRED EXPERIENCE, SKILLS & QUALIFICATIONS :
– Hands-on experience implementing GenAI and Machine Learning technologies within a SaaS or Cloud application environment.
– Experience in designing and implementing large-scale Data Lakes and Data Warehouses with a focus on long-term scalability.
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