Data Engineer

Description :

About the Role :

Data Engineer

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 :

• Bachelor’s or Master’s 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