Location: Lahore, Karachi, Islamabad
About Liquid Technologies
Liquid Technologies is a future-focused technology company delivering AI, software development, data engineering, DevOps, product design, and digital transformation solutions. Our multidisciplinary team of 100+ specialists has spent over a decade helping startups, enterprises, and organizations across healthcare, energy, manufacturing, finance, and beyond turn bold ideas into scalable, real-world products — from proprietary platforms like Vidan.AI (AI-powered video analytics) and Vitalog (digital health) to custom enterprise SaaS and machine learning pipelines. We pair deep technical expertise with a design-thinking approach and a strong commitment to responsible, ethical AI — engineering impact, not just code.
Role
We are seeking a skilled Senior Data Engineer with strong expertise in Databricks and Snowflake to design, build, and optimize scalable data pipelines. You will work on high-performance data processing workflows that support our platform, with a focus on real-time analytics, large-scale data transformations, and efficient data modeling. . The role covers ETL, Synapse, Databricks, Data Factory, SSIS, and Power BI integration. If you have experience with distributed data systems, cloud-based data platforms, and modern data engineering best practices, we’d love to hear from you.
Responsibilities
- Build and optimize data pipelines that ingest, validate, and transform core banking data (accounts, transactions, balances, parties, fees) from multiple source systems into our Databricks/Delta Lake lakehouse.
- Scale and evolve a multi-tenant architecture, ensuring tenant isolation, efficient partitioning, and consistent schema evolution as we onboard new banks.
- Own CI/CD for the data platform, including GitHub Actions workflows, SQLMesh plan/apply lifecycle, and Databricks deployment automation.
- Develop and integrate ML models, including propensity scoring, churn prediction, segmentation, and customer scoring models that feed directly into analytics and decisioning layers.
- Ensure pipeline reliability through monitoring, alerting, and robust data validation across tenants and environments.
- Design and maintain 300+ SQL and Python data models across Bronze, Silver, and Gold layers using SQLMesh, with an emphasis on clean abstractions, reusability, and correctness.
- Own the metrics layer, defining and validating gold-standard business metrics (revenue, attrition, household analytics, segmentation, balance projections) used by dashboards and APIs.
- Champion data quality by writing SQLMesh audits, unit tests, and enforcing schema contracts to ensure downstream consumers can trust the data.
- Collaborate with product and banking domain experts to translate business requirements into well-modeled, documented, and performant data assets.
- Drive documentation and discoverability, ensuring data models are self-describing and easily understood by analysts and product teams.
Requirements
- 8+ years of software engineering experience, with deep expertise in data engineering and strong exposure to analytics engineering or data modeling.
- Production experience with SQLMesh or dbt, including building, testing, and deploying transformation projects (SQLMesh strongly preferred).
- Hands-on experience with Databricks or Snowflake, operating pipelines and warehouses in production environments.
- Advanced SQL skills, including complex window functions, CTEs, incremental logic, and performance-optimized aggregations.
- Proficiency in Python, especially for PySpark transformations, data validation, and pipeline automation.
- Strong understanding of dimensional modeling, medallion/layered architectures, and data quality best practices.
- Experience with CI/CD for data, including automated testing, version control, and deployment pipelines.
Nice-to-Have Qualifications
- Experience building or operationalizing ML models (propensity, churn, segmentation) within a data platform.
- Background in banking, financial services, or fintech data domains.
- Familiarity with Azure services (ADLS Gen2, Azure SQL, Databricks on Azure).
- Experience with multi-tenant SaaS data architectures, including schema isolation and tenant-aware partitioning.
- Exposure to data mesh concepts and domain-oriented data ownership.
- Familiarity with Databricks Unity Catalog, Auto Loader, or Databricks Workflows.
- Experience with Linear, GitHub Actions, or similar project management and CI/CD tooling.
