Description :

About the Role :

We are seeking a highly experienced Data Engineer with 5+ years of hands-on expertise to join our data platform and analytics engineering team. The ideal candidate will specialize in designing, building, and maintaining scalable, reliable, and high-performance data pipelines and analytical data platforms. This role requires close collaboration with data scientists, analysts, product teams, and business stakeholders to ensure trusted, well-modeled, and production-ready data for analytics and AI use cases.

Key Responsibilities :

- Design, develop, and maintain end-to-end data pipelines for batch and streaming data.

- Build scalable, reliable, and efficient ETL/ELT workflows using modern data engineering tools.

- Develop and optimize data models for analytics, reporting, and machine learning use cases.

- Implement data ingestion from multiple sources including databases, APIs, files, and event streams.

- Work closely with data scientists and analysts to support analytical and ML workloads.

- Ensure data quality, consistency, validation, and monitoring across data pipelines.

- Optimize performance and cost for large-scale data processing systems.

- Collaborate with cloud and platform teams to deploy and manage data infrastructure.

- Implement data governance, security, access controls, and compliance best practices.

- Develop and maintain documentation for data pipelines, models, and architectural decisions.

- Monitor pipeline health, troubleshoot failures, and implement proactive alerting.

- Mentor junior data engineers and contribute to data engineering best practices and standards.

Required Skills & Qualifications :

- Bachelors or Masters degree in Computer Science, Data Engineering, Information Systems, or a related field.

- 5+ years of professional experience in data engineering, analytics engineering, or data platform roles.

- Strong proficiency in Python for data processing and pipeline development.

- Advanced hands-on experience with SQL for transformations, analytics, and performance tuning.

- Strong experience with Databricks (Spark, Delta Lake, workflows, notebooks).

- Strong hands-on experience with Snowflake, including data modeling and performance optimization.

- Experience building transformation layers using dbt (models, tests, macros, documentation).

- Solid understanding of data warehousing concepts, dimensional modeling, and analytical data models.

- Experience working with batch and streaming data pipelines.

- Familiarity with orchestration tools such as Airflow or equivalent.

- Experience working in cloud environments (AWS, GCP, or Azure).

- Strong problem-solving skills and ability to work with cross-functional stakeholders.

Good to Have :

- Experience with big data technologies such as Spark, Kafka, or Hadoop.

- Exposure to real-time data streaming and event-driven architectures.

- Knowledge of MLOps concepts and supporting ML workflows with data pipelines.

- Familiarity with MLflow, Feature Stores, or model data versioning.

- Hands-on experience with Python data science libraries such as :

a. Pandas, NumPy

b. Scikit-learn

c. Statsmodels

- Experience supporting data science and machine learning teams with curated datasets.

- Experience building analytics-ready datasets for dashboards and executive reporting.

- Knowledge of CI/CD practices for data pipelines.

- Experience with monitoring, logging, and observability for data platforms.

- Exposure to business analytics, KPI tracking, or executive dashboards.

- Domain experience in finance, retail, healthcare, manufacturing, or SaaS.

- Prior experience mentoring teams or leading data engineering initiatives.

Relevant Certifications (Preferred) :

- Databricks Certified Data Engineer

- Snowflake SnowPro certifications

- AWS Data Analytics / Data Engineer certifications

- Google Professional Data Engineer

- Azure Data Engineer Associate