Job Description

Job Description: Data Engineer

Location: Mumbai / Pune

Experience: 5-10 years

Role Summary

The Data Engineer is responsible for building, maintaining, and operating reliable, scalable data systems that support analytics, reporting, and machine learning use cases.

The role focuses on deterministic data transformations, data quality, and system correctness, and partners closely with Data Scientists to operationalize models.

Key Responsibilities

Data Pipelines & Orchestration

• Design, build, and maintain ETL / ELT pipelines for analytics and ML workloads
• Use modern orchestration and transformation tools such as:
• Prefect, Airflow, Dagster, n8n
• dbt for transformations and data modelling
• Manage scheduling, retries, dependencies, backfills, and failure recovery

Data Modeling & Analytics Enablement

• Design and maintain analytics-ready schemas (fact/dimension models, marts)
• Implement business logic and metrics deterministically in SQL/dbt
• Ensure metric definitions are consistent and reproducible across teams

ML Enablement (Engineering)

• Build pipelines for:
• Model training data preparation
• Model evaluation data
• Batch or real-time inference
• Implement feature engineering logic provided by Data Scientists
• Compute and expose model performance metrics

Data Quality, Reliability & Observability

• Implement data validation, freshness checks, and anomaly detection
• Monitor pipeline health, performance, and costs
• Ensure data lineage, auditability, and reproducibility

Required Skills & Qualifications

• Strong proficiency in SQL and Python
• Experience with data warehouses / lakes
• Hands-on experience with:
• Orchestration tools (Prefect, Airflow, Dagster, n8n)
• dbt or equivalent transformation frameworks
• Solid understanding of:
• Statistics fundamentals (distributions, aggregates, confidence intervals)
• Model evaluation metrics (precision, recall, RMSE, AUC, etc.)
• Strong systems thinking: scalability, performance, fault tolerance

Accountability & Success Criteria

• Data pipelines are reliable, reproducible, and observable
• Metrics and features are computed correctly and consistently
• Models can be trained, evaluated, and served without data issues

Explicit NonResponsibilities

• Building or operating production data pipelines
• Orchestration, scheduling, or data platform reliability
• Managing SLAs for data or inference systems

More Info

Education