Business Insights Engineer at Unilode Aviation

Kodigehalli, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

11 Aug, 26

Salary

0.0

Posted On

13 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

PySpark, SQL, Databricks, Delta Lake, AWS, Dimensional Modelling, CI/CD, Git, ETL/ELT, Data Modelling, Semantic Layer Development, BI Tools, Data Governance, Unity Catalog, Star Schema, Data Profiling

Industry

Airlines and Aviation

Description

Description The role of the Business Insights Engineer is to support the development of a scalable, governed, and selfservice analytics capability across Unilode by designing and maintaining trusted data models, semantic layers, and analytical datasets that enable reporting, automation, advanced analytics, and the generation of business insights. The role focuses on translating business processes into structured and optimised data solutions, improving data accessibility and adoption, and supporting the ongoing evolution of Unilode’s Enterprise Data Warehouse (EDW) and analytics engineering practices. The Business Insights Engineer works closely with subject matter experts, analysts, engineers, and stakeholders to ensure data products are reliable, maintainable, and aligned with business needs. Key Responsibilities Data Modelling & Analytical Design Business Insights, Drives results, Action Oriented Design and maintain logical and physical data models aligned to business processes and analytical requirements. Translate operational and business requirements into scalable and governed data structures. Apply industry-recognised modelling approaches such as Kimball or Inmon methodologies. Conduct data profiling and source system analysis to support design decisions. Identify opportunities to improve data model performance, usability, and maintainability. Balance business value with long-term cost of ownership and scalability considerations. Data Products & Semantic Layer Development Drives results, Customer Focus, Ensures Accountability Build and maintain curated datasets and semantic layers to support dashboards, reporting, ML models, and data products. Treat data as a product to improve discoverability, usability, and adoption across the organisation. Develop automated reporting, dashboards, and alerting solutions using BI and visualisation tools. Support scalable analytics by enabling self-service access to governed datasets. Ensure data products are reliable, optimised, and aligned with business expectations. Data Pipeline Engineering & Automation Action-oriented, Drives results, Ensures Accountability Build and maintain scalable ETL/ELT pipelines using PySpark, SQL, and Databricks. Support automation of legacy reporting and manual data processes. Contribute to improving the speed, reliability, and efficiency of data pipelines and workflows. Apply CI/CD principles, version control, and release management practices to data engineering activities. Ensure solutions are maintainable, testable, and aligned to engineering standards. Stakeholder Collaboration & Analytics Enablement Collaborate, Customer Focus, Courage Work closely with analysts, business users, product teams, and technical stakeholders to understand requirements and priorities. Communicate technical concepts clearly to non-technical audiences. Support analysts and data consumers in understanding and using available datasets effectively. Provide guidance and mentorship to analytics teams on data modelling and engineering best practices. Encourage adoption of self-service analytics capabilities across the organisation. Data Governance & Quality Assurance Ensures Accountability, Business Insights Support implementation of data governance standards, policies, and controls. Create and maintain clear documentation for data models, workflows, and use cases. Perform unit, integration, and efficiency testing to validate data quality and credibility. Support initiatives that improve trust, consistency, and transparency in enterprise data. Identify and escalate data quality issues, inconsistencies, or risks. Continuous Improvement & Innovation Business Insights, Courage, Action-Oriented Identify opportunities to improve data quality, efficiency, and analytics engineering practices. Investigate issues in systems, processes, and services and support the implementation of preventative measures. Continually refine data models in response to user feedback and organisational change. Keep up to date with emerging technologies, tools, and engineering techniques. Explore and leverage AI capabilities to improve coding practices, modelling approaches, and insight generation. Our Values in Action: Be humble and curious: Continuously seeks to understand business processes, data structures, and emerging technologies to improve analytical outcomes. Inspire, empower and prosper: Supports colleagues and stakeholders by enabling easier, more reliable access to trusted data. Team up to be better: Works collaboratively across analytics, engineering, product, and business teams to deliver scalable solutions. Be passionate about our customers: Focuses on delivering data products and insights that improve decision-making and business performance. Take ownership and get things done: Takes accountability for delivering reliable, maintainable, and high-quality data solutions. Be eager to win: Continuously improves data engineering practices, automation, and analytics capability. Build a better future: Supports the development of scalable, governed, and sustainable enterprise data foundations. The Small Print The role of Business Insights Engineer focuses on designing, building, and maintaining scalable analytical data solutions that enable trusted, self-service analytics across the organisation. The role requires strong technical capability, structured problem-solving, and effective collaboration with both technical and non-technical stakeholders. This document outlines the key responsibilities and expectations of the role, but is not an exhaustive list. Responsibilities may evolve in line with changes in technology, data strategy, organisational priorities, and analytics maturity. The role requires adaptability, continuous learning, and a proactive approach to improving data quality, scalability, and insight generation across the enterprise. Requirements Skills & Experience Hands-on experience in data engineering, analytics engineering, or related fields. Experience working with Databricks, including Jobs/Workflows, repositories, cluster policies, and Unity Catalog. Strong proficiency in PySpark and SQL with experience building scalable ETL/ELT pipelines. Solid understanding of Delta Lake architecture including Bronze, Silver, and Gold design patterns. Experience working with AWS services including S3, IAM, and secure data architectures. Understanding of dimensional modelling and star schema design. Experience implementing CI/CD for data pipelines using Git-based workflows. Strong analytical and problem-solving skills. Ability to work effectively across engineering, analytics, product, and business teams. Strong communication skills with the ability to support, mentor, and guide stakeholders. Experience supporting semantic layer development or self-service analytics environments. Exposure to machine learning pipelines or advanced analytics environments. Experience with BI and visualisation platforms. Familiarity with data governance and metadata management practices.

Responsibilities

Design and maintain scalable data models, semantic layers, and ETL/ELT pipelines to enable self-service analytics. Collaborate with stakeholders to translate business processes into governed data products and improve data accessibility.