Data Engineer

Clinisys

Data Engineer responsible for building and operating reliable data platforms and pipelines at Clinisys. Collaborating with teams to enhance data quality for analytics and AI initiatives.

Posted 6/18/2026full-timeBangalore • 🇮🇳 IndiaMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AirflowCloudETLPythonSparkSQL

About the role

Key responsibilities & impact

Evaluate Cloud Architecture: Provide technical leadership in evaluating, selecting, and implementing the company’s foundational unified cloud data platform.
Support BI and Analytics: Partner closely with BI and business teams to deliver high-quality, analytics-ready datasets, custom schemas, and self-service data consumption models.
Design and Build Pipelines: Construct and maintain scalable batch and streaming ETL/ELT pipelines to integrate enterprise applications, product systems, and external sources.
Ensure Data Quality: Implement automated quality checks, reconciliation processes, and lineage documentation to guarantee accurate business metrics and rapid issue remediation.
Optimize Workflow Operations: Establish strong pipeline testing, monitoring, alerting, and CI/CD practices to meet data freshness and reporting SLAs.
Control Costs and Performance: Optimize data storage and compute efficiency through strategic partitioning, indexing, query tuning, and workload management.
Create Reusable Frameworks: Build and maintain reusable code libraries for data ingestion, transformation, and validation to accelerate delivery of business insights.
Enforce Security and Governance: Partner with Security and Compliance to implement data access controls, masking, encryption, retention policies, and robust auditability.
Document and Share Knowledge: Maintain clear documentation for pipelines, data products, and operational runbooks to ensure team supportability and data definitions.

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field, or equivalent work experience.
Proven experience (5+ years) building and operating production data pipelines and data models.
Strong proficiency in SQL and at least one programming language commonly used for data engineering (e.g., Python).
Experience with modern data processing and orchestration concepts (e.g., Spark, dbt, Airflow-like orchestration) and cloud data platforms (e.g., Snowflake, Databricks, MS Fabric, etc.).
Knowledge of data modeling techniques (dimensional, normalized, and data product approaches) and data quality practices.
Familiarity with APIs, file-based ingestion, and event/stream processing patterns.
Strong troubleshooting skills and ability to work across teams to resolve data issues quickly and permanently.

Benefits

Comp & perks

Up to 20% travel may be required
Moderate lifting/carrying 15-44 lbs; use of fingers, walking/standing 2-6 hours

ATS Keywords

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Hard Skills & Tools

SQLPythonETLELTdata modelingdata qualitydata ingestiondata transformationdata validationdata processing

Soft Skills

technical leadershiptroubleshootingcollaborationdocumentationcommunicationproblem-solvingorganizational skillsattention to detailadaptabilitytime management