Data Engineer
Konovo is a global healthcare intelligence company on a mission to transform research through technology- enabling faster, better, connected insights. Konovo provides healthcare organisations with access to over 2 million healthcare professionals—the largest network of its kind globally. With a workforce of over 200 employees across 5 countries: India, Bosnia and Herzegovina, the United Kingdom, Mexico, and the United States, we collaborate to support some of the most prominent names in healthcare. Our customers include over 300 global pharmaceutical companies, medical device manufacturers, research agencies, and consultancy firms. As we transition from a service-oriented model to a product-driven platform, we are expanding our hybrid Bengaluru team. We are looking for an experienced data engineer to contribute to our mission by deep-diving into business problems, building out our data lakehouse and contributing to our advanced analytics. As a Konovo engineer, you will unlock the power of our data, learn a breadth of technologies and aspects of data engineering, help us optimize and solve some of our greatest challenges! How You'll Make an Impact Build and own production-grade batch ELT pipelines in Databricks/Spark (15-minute cadence during working hours; hourly off-hours) Design, scale, and operate our lakehouse using medallion patterns (Bronze/Silver/Gold), including incremental loads, backfills, and late-arriving data handling Create curated, well-documented data products that are reusable across teams and trusted for decision-making Improve reliability through automated testing, monitoring/alerting, and clear SLAs for critical pipelines and tables Partner with engineering and business stakeholders to translate needs into durable data models (not one-off extracts) Ensure data governance and security expectations are met through disciplined implementation (access controls, lineage, and quality standards) What We're Looking For 5+ years building and operating production data pipelines (data engineering—not primarily BI/analytics or data science) Strong SQL plus strong data modeling skills (dimensional and/or lakehouse modeling) Hands-on Databricks + Spark experience in production (debugging, performance tuning, cost awareness) Experience building batch ELT at frequent cadence (e.g., 15-minute schedules), including idempotency, backfills, and late-arriving data patterns Experience with orchestration and transformation tooling (e.g., Airflow + dbt) and modern development practices (version control, CI/CD) Strong data quality discipline: automated tests/expectations, monitoring/alerting, and clear SLAs for critical datasets Ownership mindset: you build it, you run it (triage, incident response, continuous improvemen...