Architect and design large enterprise-level analytics solutions, leveraging big data platforms such as Databricks, Snowflake, Hive, Hadoop, and PostgreSQL.
Develop data pipelines for big data ingestion using tools like Spark, Informatica PowerCenter & BDM, and Apache NiFi/Airflow for continuous data flow automation.
Work with enterprise systems such as ERPs (SAP, Oracle), RDBMS, CRMs, and logistics tools to create analytics solutions that improve business functions across the organization.
Ensure scalability, optimization, and automation for all data solutions to meet high-performance and high-availability requirements.
Develop APIs, microservices, and platform-based solutions for data integration, enabling seamless data interaction across systems and platforms.

Lead a cross-functional team of data engineers, data architects, data scientists, DevOps, scrum masters, and product owners to build and deploy end-to-end solutions.
Take ownership of data engineering design and data management best practices, including dimensional modeling, data warehousing, and data mining.
Collaborate with leadership to understand business needs and propose optimal data-driven solutions.
Ensure continuous improvement of data processes, focusing on efficiency, automation, and reducing manual work.

Lead the deployment of big data ingestion jobs using Spark and other industry-standard tools on Hadoop, Databricks, and NoSQL platforms.
Ensure the integration of cloud-based solutions using Azure and AWS platforms, enabling seamless scaling and flexibility.
Work with IoT data and ensure the integration of data sources for analytics solutions.

Leverage your expertise to integrate advanced analytics and machine learning capabilities into the platform for predictive and prescriptive analytics.
Build solutions that harness big data for actionable insights, forecasting, predictive modeling, and other analytics needs.

Identify opportunities for process optimization and automation, ensuring that all data operations are efficient and reduce the manual effort involved.
Work towards building a data-driven culture within Honeywell Connected Enterprise, fostering an environment that embraces automation and continuous improvement.

Contribute to the creation of a new platform by working on both frontend and backend aspects, including data storage, APIs, microservices, and data orchestration.
Enable seamless integration between existing and new platforms, ensuring a robust and scalable architecture.

Bachelor's/Master's degree in Computer Science, Engineering, Applied Mathematics, or a related field.
12+ years of experience in data engineering, data design, and enterprise data management.
Strong expertise in architecting large-scale analytics projects and big data platforms (Databricks, Snowflake, Hadoop, etc.).
4+ years of hands-on experience with tools like Spark, Informatica PowerCenter & BDM, Apache NiFi/Airflow, and cloud platforms like Azure and AWS.
Experience in dimensional modeling, data warehousing, and data mining.
Strong knowledge of big data ingestion, processing, and optimization, with a focus on scalability and performance.
Expertise in cloud-based data solutions (Azure, AWS), with experience in developing, deploying, and managing cloud data pipelines.
Proven experience in data integration, building APIs, and working with microservices architectures.

Experience with industrial data (IoT, SAP, and other operational systems).
Proficiency in data science techniques, including predictive modeling, machine learning, and AI-based analytics.
Knowledge of advanced data analytics platforms such as Power BI or Tableau.
Ability to work with non-relational databases (Cassandra, NoSQL, etc.) and experience in advanced query optimization.

Lead Data Engineer