Data Engineer - Python/SQL/ETL

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 :
• Pandas, NumPy
• Scikit-learn
• 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
  

(ref:hirist.tech)