Senior Data Pipeline Engineer

, including details of their CTC, ECTC, notice period, and a short overview of their relevant ML and Data Engineering work.

About the Role

We are seeking an experienced Data Engineer (5+ years) with strong, hands-on experience in ML / AI data workflows. You will play a critical role in building feature pipelines, model-serving data flows, and end-to-end orchestration that powers our production ML systems.

This is a fully remote, ownership-driven position.

Key Responsibilities

ML / AI Data Engineering

• Build and maintain feature engineering pipelines for ML model training and inference.
• Develop and optimize model-serving data pipelines ensuring low-latency and reliable delivery.
• Design and orchestrate end-to-end ML workflows (Airflow, Prefect, Dagster, Kubeflow, etc.).
• Work closely with Data Scientists and ML Engineers to productionize ML models.
• Implement automated dataset versioning, feature stores, and reproducibility frameworks.
• Build scalable data foundations required for MLOps : monitoring, retraining triggers, model data validation.

Data Pipelines & ETL

• Design and build high-performance ETL / ELT pipelines for structured and unstructured data.
• Manage ingestion from APIs, databases, files, event streams, and cloud storage.
• Ensure pipelines are fault-tolerant, well-monitored, and automated.

Data Modelling & Data Warehousing

• Build and maintain data models, marts, and warehouse layers to support analytics and ML pipelines.
• Translate ML feature requirements into clean and optimized data structures.

Data Quality & Governance

• Implement schema validation, data quality checks, and automated monitoring.
• Maintain metadata, lineage, and documentation for all data flows.

Cloud & Infrastructure

• Develop cloud-native data workflows (AWS / Azure / GCP).
• Work with data storage and compute systems like S3, BigQuery, Snowflake, Databricks, Redshift, etc.
• Ensure performance optimization, scaling, and cost-efficiency.

DevOps, CI / CD & Automation

• Build CI / CD pipelines for data and ML workflows.
• Containerize pipelines using Docker, and manage deployments via Git-based workflows.
• Automate scheduling, builds, and monitoring for data and ML systems.

Required Skills & Experience

• 5+ years of experience as a Data Engineer.
• Major Requirement (Non-Negotiable) : Strong experience working on ML / AI projects, including :
• ML feature pipelines
• Model-serving data workflows
• ML orchestration (Airflow, Prefect, Dagster, Kubeflow, etc.)
• Strong in Python, SQL, and ETL frameworks.
• Experience with big data technologies (Spark, PySpark, Databricks).
• Hands-on with cloud platforms (AWS / Azure / GCP).
• Experience with CI / CD, Docker, Git, APIs.
• Ability to work independently and in cross-functional remote teams.
• Excellent communication and documentation skills.

Nice-to-Have Skills

• Tools : MLflow, Vertex AI, SageMaker, Azure ML
• Streaming : Kafka, Kinesis, Pub / Sub
• Data quality frameworks : Great Expectations, Soda, Pandera

Why Join Us

• Fully remote with flexible schedule
• Work on real-world ML / AI production systems
• High ownership + direct architectural influence
• Opportunity to collaborate with advanced Data Science & ML teams