Senior Data Engineer

Job Description

Senior Data Engineer

Full-time

Legal Entity: Bosch Global Software Technologies Private Limited

Company Description

Bosch Global Software Technologies Private Limited is a 100 PERCENT owned subsidiary of Robert Bosch GmbH, one of the world's leading global suppliers of technology and services, offering end-to-end Engineering, IT, and Business Solutions. With over 27,000 associates, its the largest software development center of Bosch outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe, and the Asia Pacific region.

Roles & Responsibilities : Data Architecture & Engineering

• Design and implement end-to-end data pipelines for ingestion, transformation, and storage of structured, semi-structured, and time-series data.
• Build both real-time and batch processing frameworks using Databricks, supporting scalable analytics and AI workloads.
• Develop and maintain ETL/ELT workflows using Python and SQL, ensuring reusability and maintainability.
• Architect and optimize data lakes/lakehouses (Azure Synapse, Delta Lake, BigQuery, or Snowflake) for efficient querying and cost control.
• Design and manage NoSQL databases (MongoDB) and time-series databases (InfluxDB, TimescaleDB, Azure Data Explorer) for sensor and operational data.
• Enable AI/ML readiness by developing feature pipelines, managing datasets, and integrating with model inference systems.

Cloud & Integration

• Orchestrate and monitor data pipelines using Azure Data Factory, Azure Functions, and Event Hub for real-time ingestion and transformation.
• Build serverless, event-driven applications using Azure Functions (Python-based), AWS Lambda, or GCP Cloud Functions.
• Implement hybrid data integration between edge, on-prem, and cloud using secure APIs, message queues, and connectors.
• Integrate data from IoT devices, ERP, MES, PLM, and simulation tools to enable enterprise-wide digital twin insights.
• Develop containerized microservices using Docker and Kubernetes to support portable, cloud-agnostic deployments across Azure, AWS, and GCP.

Performance, Security & Governance

• Implement frameworks for data quality, lineage, and observability (Great Expectations, Azure Purview, OpenMetadata).
• Enforce data governance, privacy, and compliance with standards such as GDPR, ISO 27001, and industry regulations.
• Optimize resource utilization and cost across compute, storage, and database layers.
• Establish data retention, access control, and lifecycle policies across multi-tenant environments.

Collaboration & Strategy

• Collaborate with cloud architects, AI/ML engineers, and domain experts to align data architecture with Industry 4.0 and Digital Twin goals.
• Evaluate and introduce emerging technologies such as vector databases, streaming analytics, and data mesh frameworks.
• Mentor junior engineers and promote best practices in Pythonic coding, DevOps, and GitOps workflows.
• Develop and maintain data engineering accelerators and reusable frameworks for internal adoption.

Qualifications Educational Qualification: Required Qualifications

• Bachelors or Masters degree in Computer Science, Data Engineering, or related field.
• 8 years of experience in data engineering, analytics, or big data systems.

Mandatory Skills:

• Strong programming skills in Python and SQL for data transformation, orchestration, and automation.
• Expertise in Azure data services (Synapse, Data Factory, Event Hub, Azure Functions, Databricks).
• Hands-on experience with MongoDB, Cosmos DB, and time-series databases such as InfluxDB, TimescaleDB, or Azure Data Explorer (ADX).
• Proven experience with streaming frameworks (Kafka, Event Hub, Kinesis) and workflow orchestrators (Airflow, Argo, or Prefect).
• Proficiency in Docker and Kubernetes for containerization and scalable deployment.
• Familiarity with data lake/lakehouse architectures, NoSQL models, and cloud-agnostic patterns.
• Knowledge of CI/CD pipelines and infrastructure-as-code tools (Terraform, Bicep, ARM templates).

Preferred Skills:

• Experience with industrial IoT, Digital Twin data models, and protocols such as OPC-UA and MQTT.
• Exposure to edge-to-cloud data flows and predictive maintenance or anomaly detection solutions.
• Knowledge of data quality, governance, and metadata management tools.
• Strong communication and analytical skills to align data solutions with business and operational KPIs.

Additional Information Position Overview

We are seeking a highly skilled Senior Data Engineer to design, build, and optimize large-scale, cloud-native data platforms that power Digital Twin and Industrial AI solutions. This role focuses on developing high-performance data ingestion and transformation pipelines that unify IoT, enterprise, and AI/ML data, enabling real-time insights, scalability, and interoperability across hybrid environments.

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