Meaningful work,
meaningful life

Key Responsibilities

• Design and architect end-to-end data platforms on cloud (Azure/AWS/GCP) with Databricks as the core analytics engine.

• Define data architecture standards, best practices, and reference architectures.

• Lead the design and implementation of data ingestion, transformation, and serving layers using modern data engineering frameworks.

• Architect Lakehouse solutions leveraging Delta Lake, Unity Catalog, and medallion architecture.

• Ensure data security, governance, performance optimization, and cost efficiency across platforms.

• Collaborate with business, analytics, AI/ML, and engineering teams to translate business needs into scalable data solutions.

• Guide and mentor data engineers and developers on architecture, design patterns, and best practices.

• Drive cloud-native design principles including CI/CD, Infrastructure as Code, and automated testing.

• Participate in solution reviews, POCs, and technology evaluations.

Required Skills & Qualifications

• 10+ years of experience in data engineering, with at least 4–5 years in an architecture or lead role.

• Strong hands-on experience with Databricks (must-have), including Delta Lake, Spark, Unity Catalog, and workflows.

• Deep experience with at least one major cloud platform: Azure, AWS, or GCP.

• Expertise in designing scalable data pipelines using Spark, Python, SQL, and/or Scala.

• Strong experience with cloud-native data services such as:

  • Azure: ADLS, ADF, Synapse

  • AWS: S3, Glue, EMR, Redshift

  • GCP: BigQuery, GCS, Dataflow

• Experience with data modeling, data warehousing, and Lakehouse architectures.

• Strong understanding of data governance, security, compliance, and data quality frameworks.

• Experience with real-time and batch data processing architectures.

• Hands-on experience with DevOps/MLOps practices, CI/CD pipelines, and IaC tools (Terraform, ARM, CloudFormation).

• Excellent communication and stakeholder management skills.