Associate Data Engineer
Joblogic · Lahore, Punjab, Pakistan ·
- Category
- Data engineering
- Experience
- 1+ years
- Company size
- 501-1000
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Review and validate data reports, dashboards, and metrics for accuracy before stakeholder use, using tools like Excel, Power BI, and AI assistants.
Established in 1998, Joblogic is the UK’s #1 Field Service Management (FSM) software platform. We are a global business with offices in the UK, Pakistan, and Vietnam. Since our management buy-out in 2013, we have grown from ~£500K ARR to ~£35M+ ARR and expanded our team from 11 to 500+ people.
Recently, we secured a strategic growth investment from Vista Equity Partners — a global technology investor specialising in enterprise software. This investment includes over £100 million in new primary capital and will fuel our next phase of growth by accelerating our AI-first roadmap, expanding our platform into CAFM (Computer-Aided Facilities Management) capabilities, and supporting our expansion across Europe and beyond.
With Vista’s backing, we’re transforming from a successful UK business into a global scaling SaaS rocket ship, and we’d love for you to join us on our journey to £100M ARR across international markets.
Joblogic provides software to service contractors who install and maintain the built environment. Our platform helps businesses streamline operations, improve profitability, ensure compliance, and achieve rapid growth. With over 100,000 users across industries including HVAC, plumbing, electrical maintenance, facilities management, and building fabric maintenance, we are entering a new era of intelligent automation, predictive maintenance, and data-driven decision-making for service firms.
About the Role
We are looking for an Associate Data Engineer to join our Data Engineering team. This is a great opportunity for someone with 6 months to 1 year of hands-on experience who wants to build a strong foundation in data pipeline development, database
management, and cloud data platforms. You will work closely with senior data engineers and analysts to build, maintain, and optimise the data infrastructure that powers reporting, analytics, and product features across the business.
Key Responsibilities
• Build, test, and maintain ETL/ELT pipelines to extract, transform, and load data from multiple internal and external sources.
• Write and optimise SQL queries for data extraction, transformation, and reporting.
• Assist in designing and maintaining data models, schemas, and database structures.
• Monitor scheduled data jobs and pipelines, troubleshoot failures, and perform root-cause analysis.
• Support data quality checks, validation, and reconciliation across systems.
• Work with senior engineers to migrate and integrate data across on-premise and cloud environments.
• Document data flows, pipeline logic, and technical processes.
• Collaborate with analysts, QA, and business teams to understand data requirements and deliver reliable datasets.
• Follow coding standards, version control practices, and participate in code reviews.
Required Skills & Qualifications
• 6 months to 1 year of experience in data engineering, database development, or a related role.
• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
• Working knowledge of SQL and relational databases (e.g., SQL Server, MySQL, PostgreSQL).
• Intermediate proficiency in Python or another scripting language for data processing.
• Understanding of ETL/ELT concepts and data warehousing fundamentals.
• Exposure to any cloud platform (Azure) is a plus.
• Familiarity with version control tools such as Git.
• Strong analytical and problem-solving skills with attention to detail.
• Good communication skills and willingness to learn in a fast-paced environment.
Good to Have
• Exposure to data pipeline/orchestration tools (e.g., Azure Data Factory, Fabric, SSIS).
• Familiarity with BI/reporting tools (Power BI).
• Basic understanding of data modelling concepts (star/snowflake schema, normalization).
• Awareness of data governance, security, and privacy best practices
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