Data Engineer

Job Requirements

English Level:Professional proficiency (written and spoken)

Domain:Life Insurance domain is required.

Technical Leadership & Architecture

Own and define enterprise data architecture leveraging Azure Data Platform, Databricks, and Snowflake.

Establish and enforce engineering standards, best practices, and design patterns for data ingestion, transformation, and analytics.

Lead architectural reviews covering scalability, performance, security, governance, and cost optimization.

Design and guide lakehouse architectures using Azure Data Lake Storage (ADLS), Databricks, and Snowflake.

Make strategic technology decisions across Azure-native, Databricks, and Snowflake ecosystems.

Delivery & Execution

Lead large-scale data migrations from on-premise or legacy platforms to Azure, Databricks, and Snowflake.

Oversee development of robust ETL/ELT pipelines using tools such as Azure Data Factory, Databricks (Spark), SQL, and Python.

Ensure data quality, reliability, observability, and performance across all pipelines.

Guide teams through iterative delivery while managing technical risks and changing requirements.

Team Enablement & Mentorship

Lead and mentor data engineers through code reviews, design sessions, and technical coaching.

Drive technical excellence and raise the maturity of data engineering practices across teams.

Support onboarding and skill development for engineers working with Azure, Databricks, and Snowflake.

Documentation & Governance

Ensure production-grade technical documentation, including architecture diagrams, data models, and operational runbooks.

Define governance standards for data security, access control, CI/CD, and version control.

Must-Have Qualifications

7+ years of experience in data engineering, with 3+ years in a Lead or Tech Lead role.

Deep hands-on experience with Databricks (Apache Spark, Delta Lake, notebooks, workflows).

Strong experience designing and operating Snowflake data warehouses.

Advanced skills in SQL and Python for large-scale data processing.

Proven experience leading complex data migrations and modernization initiatives.

Strong understanding of data modeling, performance tuning, and cost optimization.

Ability to manage multiple workstreams and shifting priorities.

Excellent English communication skills for client leadership, workshops, and documentation.