Senior Technical Lead

Job Summary

Key Responsibilities:

Design, build, and optimize scalable batch and streaming data pipelines using distributed framework like Spark/Databricks/Kafka.

Data and ML Engineering thought leadership (What, Why & How) ) - Design & Code - robust data models, feature pipelines, and ETL/ELT frameworks for analytics and ML.

Ensure data quality, observability, lineage, and performance across data platforms.

Build and refine ML models end‑to‑end: feature engineering, training, evaluation, and deployment.

Partner with data scientists to convert prototypes into production‑grade ML solutions.

Implement CI/CD, model versioning, monitoring, and automation across data and ML workflows.

Product Driven Mindset: Collaborate with engineering, product teams to deliver data‑driven outcomes.

Required Skills

7+ years of experience in ML-Data Engineering development.

Strong SQ/NoSQL, Python, Py Spark, and ML Models Lifecycle & Frameworks (Ml Flow, Spark-ml), Orchestration (Airflow/Oozie/Dagster etc)

Expertise in Big Data modeling, Distributed processing, and Lake & Warehouse architectures at large operational scale.

Hands‑on with ML lifecycle tools (MLflow, Feature Store, model monitoring, Evaluation).

• Strong Analytical & Problem Solving Skills

• Data/Process Intensive Design/Architecture, Strong debugging, optimization.

• Basic hold on foundational modelling concepts & algorithms such as

• Regression, Classification and Statistical models.

Good Hold on Concepts- Distributed File Formats, Open table Formats, Distributed transaction management, Workload Parallelizing.

• Jands on
• Unix, Hadoop, Object store fundamental operations & commands

Basic skilled with containerized processing (Docker + K8s)

Key Responsibilities

Key Responsibilities:

Design, build, and optimize scalable batch and streaming data pipelines using distributed framework like Spark/Databricks/Kafka.

Data and ML Engineering thought leadership (What, Why & How) ) - Design & Code - robust data models, feature pipelines, and ETL/ELT frameworks for analytics and ML.

Ensure data quality, observability, lineage, and performance across data platforms.

Build and refine ML models end‑to‑end: feature engineering, training, evaluation, and deployment.

Partner with data scientists to convert prototypes into production‑grade ML solutions.

Implement CI/CD, model versioning, monitoring, and automation across data and ML workflows.

Product Driven Mindset: Collaborate with engineering, product teams to deliver data‑driven outcomes.

Skill Requirements

Required Skills

7+ years of experience in ML-Data Engineering development.

Strong SQ/NoSQL, Python, Py Spark, and ML Models Lifecycle & Frameworks (Ml Flow, Spark-ml), Orchestration (Airflow/Oozie/Dagster etc)

Expertise in Big Data modeling, Distributed processing, and Lake & Warehouse architectures at large operational scale.

Hands‑on with ML lifecycle tools (MLflow, Feature Store, model monitoring, Evaluation).

• Strong Analytical & Problem Solving Skills

• Data/Process Intensive Design/Architecture, Strong debugging, optimization.

• Basic hold on foundational modelling concepts & algorithms such as

• Regression, Classification and Statistical models.

Good Hold on Concepts- Distributed File Formats, Open table Formats, Distributed transaction management, Workload Parallelizing.

• Jands on
• Unix, Hadoop, Object store fundamental operations & commands

Basic skilled with containerized processing (Docker + K8s)

Other Requirements

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