Company Overview:

LUMIQ is a data transformation company that specializes in building high-performance data platforms for the financial services industry. By leveraging cloud-native technologies and advanced data engineering practices, LUMIQ enables insurance companies and financial institutions to turn complex data silos into actionable business intelligence. The company operates at the intersection of deep domain expertise and cutting-edge engineering, helping clients modernize their data infrastructure to drive faster, data-backed decision-making at scale.

Role Overview:

As a Data Engineer at LUMIQ, you will be responsible for designing, building, and maintaining robust data pipelines that power our clients' analytical ecosystems. You will work closely with cross-functional teams, including data scientists, product managers, and client stakeholders, to translate complex business requirements into scalable technical solutions. Your work will directly impact the efficiency of data delivery, ensuring that our clients have reliable, high-quality data to fuel their critical business operations and strategic initiatives.

Key Responsibilities:

- Architect and develop scalable ETL/ELT pipelines to ingest and process large volumes of structured and unstructured data for financial analytics.

- Optimize data processing workflows using distributed computing frameworks to ensure high performance and cost-efficiency for client platforms.

- Design and maintain data models that support complex reporting requirements and downstream machine learning applications.

- Automate data orchestration and scheduling processes to ensure timely and accurate data availability for business stakeholders.

- Collaborate with engineering teams to troubleshoot data quality issues and implement automated monitoring solutions to maintain system reliability.

Required Skillset:

- Demonstrated expertise in Python for data manipulation and building complex backend services.

- Advanced proficiency in SQL for querying, performance tuning, and managing large-scale relational databases.

- Hands-on experience in building and managing distributed data processing systems using Spark and Hadoop ecosystems.

- Proven ability to design and manage complex workflow dependencies using Apache Airflow.

- Strong analytical mindset with the ability to communicate technical data challenges to non-technical stakeholders effectively.

- Ability to thrive in a hybrid work environment based in Mumbai, collaborating seamlessly with distributed teams.

- 3 - 6 years of professional experience in data engineering or a related technical role.