Data Scientist / Analytics Engineer

We are seeking a highly experienced Data Engineer with 4+ 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.

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.

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

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.

• 4+ 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.
• Experience working in cloud environments (AWS, GCP, or Azure).
• 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:

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.

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

Prior experience mentoring teams or leading data engineering initiatives.

Databricks Certified Data Engineer

Snowflake SnowPro certifications

AWS Data Analytics / Data Engineer certifications

Google Professional Data Engineer

Azure Data Engineer Associate