Lead Data Engineer

Location: Any

Experience: 7+ Years

Notice: Immediate or Serving Notice (15 Days)

Role Summary

We are hiring a Senior Data Platform Engineer to build and scale up-to-date data infrastructure, data pipelines, and cloud-based data platforms supporting analytics, business intelligence, and machine learning systems.

The ideal candidate has strong experience in ETL/ELT development, Machine learning, distributed data systems, real-time streaming, and cloud data platforms (AWS/GCP/Azure), along with expertise in data governance, data modeling, and data observability.

Core Skills

Data Engineering, Data Platform, ETL, ELT, Data Pipelines, Big Data, Distributed Systems, Data Architecture, Data Modeling, SQL, Python, (AWS, GCP, Azure), Snowflake, BigQuery, Redshift, Apache Airflow, Dagster, Prefect, Apache Kafka, Apache Flink, AWS Kinesis, dbt, Terraform, Data Governance, Data Quality, Data Observability, Machine Learning Pipelines, Data Mesh, PII Compliance, SOX Compliance

Key Responsibilities

- Build and maintain scalable ETL/ELT pipelines for batch and real-time data processing
- Design and optimize data platform architecture and data infrastructure
- Develop real-time streaming pipelines using Kafka / Flink / Kinesis
- Implement data modeling and transformation frameworks (dbt, SQL)
- Ensure data reliability, scalability, and performance optimization
- Robust ML platform / MLOps background: Hands-on experience building or operating model training, batch inference, real-time inference, release, and monitoring systems in production.
- Feature store depth: Experience with online/offline feature stores, point-in-time correctness, training-serving consistency,



low-latency feature retrieval, and feature lifecycle/governance. Experience with Chalk or an equivalent framework is highly relevant.
- Cloud + infra fluency: Strong AWS-heavy experience, specifically with SageMaker, S3, Lambda, Kinesis, DynamoDB, Kubernetes, Docker, and Terraform.
- Establish data governance, data lineage, access control, and compliance frameworks
- Build data observability systems (data quality, schema validation, monitoring dashboards)
- Collaborate with data science, analytics, product, and engineering teams
- Support machine learning pipelines and feature engineering workflows
- Mentor engineers and drive best practices in data engineering

Required Qualifications

- 7+ years of experience in Data Engineering / Data Platform Engineering
- Strong expertise in Python and SQL
- Strong expertise in ML
- Experience with cloud platforms (AWS)
- Hands-on experience with data warehouses (Snowflake / BigQuery / Redshift)
- Experience with workflow orchestration tools (Airflow / Dagster / Prefect)
- Knowledge of real-time data streaming technologies (Kafka / Flink / Kinesis)
- Experience with Infrastructure as Code (Terraform)
- Strong understanding of data modeling, distributed systems, and system design

Strong Signals (Good Candidates)

- Experience with Kafka / streaming pipelines
- Built scalable distributed systems
- Hands-on with dbt / data modeling
- Worked on ML pipelines / feature stores
- Experience with Terraform / Infra as Code
- Designed data platform architecture end-to-end
- Implemented data governance & compliance frameworks
- Experience with Data Mesh / domain-driven design
- Led teams / mentored engineers
- Built self-service data platforms

📌 Data Engineer (Pune)
🏢 Ratna Global
📍 Pune