Employment Type : Full-Time
Hope you are doing well. Serve as a senior technical owner for core components of the Data Lakehouse and ML Platform, including data ingestion, feature pipelines, metadata, and orchestration. Required Qualifications Bachelor’s degree in Computer Science or a related technical field, with 7+ years of experience building and operating data platforms in AWS. Looking forward to hearing from you.
We are currently looking for a Sr Data Engineer – ML Platform professional with strong experience in:
Python & SQL
AWS (Glue, Lambda, ECS Fargate)
Spark/PySpark
Snowflake
MLOps & ML Platforms
Docker & CI/CD Pipelines
Airflow/Dagster
Key Responsibilities:
Architect and implement scalable ML Platform capabilities that support the full ML lifecycle: data preparation, feature engineering, model training, deployment, monitoring, and retraining.
Partner closely with Data Science teams to operationalize machine learning, forecasting, and simulation models, ensuring reproducibility, reliability, and performance in production.
Design and maintain MLOps pipelines and frameworks for model versioning, promotion, rollback, and monitoring across environments.
Establish platform-level CI/CD standards for data and ML workloads, including automated testing, validation, and quality checks.
Collaborate with the Digital Development team to expose data and ML capabilities via APIs and services that support customer-facing applications.
Lead the design and delivery of Snowflake-based analytical and feature-ready data models to support BI and ML use cases.
Define and enforce data, feature, and model governance standards, including lineage, metadata, access control, and auditability.
Implement automated data and model quality assurance, drift detection, and operational monitoring to ensure platform reliability.
Build and operate containerized data and ML services using Docker, ECS Fargate, and Infrastructure as Code (CDK).
Act as a technical mentor to other engineers and influence architectural direction across the analytics and ML ecosystem.
Demonstrated experience designing or contributing to a shared ML Platform or MLOps framework used by multiple teams.
Expertise in Python and SQL, with a strong track record of production-grade data and ML systems.
Strong hands-on experience with MLOps best practices, including:
Model lifecycle management and deployment patterns
Feature engineering and reusable feature pipelines
Experiment tracking, reproducibility, and model governance
Model performance monitoring and drift detection
Advanced knowledge of AWS services including Glue, Lambda, ECS Fargate, and Apache Spark, with experience operating them at scale.
Strong experience delivering containerized, infrastructure-as-code solutions using Docker and CDK.
Deep understanding of data warehousing, lakehouse, and ML-ready data architectures, including Delta Lake; Snowflake experience is a strong plus.
Experience with Arrow-based data and streaming technologies (ADBC, Arrow?ODBC, PyArrow) a plus.
Experience with Airflow or Dagster (or similar orchestration platforms) for managing complex data and ML workflows a plus.
Proven ability to influence standards, architecture, and best practices across engineering and data science teams.
Excellent communication skills, with the ability to translate complex platform and ML concepts for technical and non-technical stakeholders.