Data Engineer

request@tytantech.com

Data Engineer

Key Responsibilities

• Design and develop scalable, efficient, and reliable

  data pipelines

  for ingestion, transformation, and storage of structured and unstructured data.
• Architect and implement modern

  data lakehouse

  and

  data warehouse

  solutions using

  Azure Synapse Analytics

  and

  Azure Data Lake

  .
• Develop and optimize complex

  data transformation workflows

  leveraging

  Apache Spark

  for parallel processing and distributed computation.
• Work closely with business and analytics teams to translate requirements into robust

  data models

  and reusable data components.
• Lead a

  multi-skilled, distributed data engineering team

  , providing technical guidance, code reviews, and best practices in data architecture and governance.
• Implement and enforce

  data quality, lineage, and performance optimization

  standards.
• Stay current with emerging

  data engineering tools, cloud-native services, and architectural patterns

  , and recommend adoption of best-fit technologies.

Requirements

• 5+ years

  of hands-on experience in

  data engineering, data transformation, and data architecture

  .
• Strong expertise in

  Azure Synapse Analytics

  ,

  Azure Data Lake

  , and

  SQL-based data modeling

  .
• Deep understanding of

  Spark clusters

  ,

  parallel data processing

  , and

  ETL optimization

  .
• Proven ability to handle

  complex data transformation

  scenarios and optimize data processing for scale and performance.
• Strong understanding of

  modern data architectures

  (e.g., lakehouse, data mesh, delta architecture).
• Experience working in

  agile environments

  and leading cross-functional data engineering teams.
• Excellent communication and documentation skills, with the ability to convey technical concepts to non-technical stakeholders.

Preferred Skills (Good to Have)

• Experience with

  Azure Data Factory

  ,

  Databricks

  , or

  Fabric

  .
• Knowledge of

  CI/CD for data pipelines

  using Git and DevOps tools.
• Exposure to

  data governance, security, and compliance frameworks

  .
• Familiarity with

  Python, PySpark

  , or

  Scala

  for custom data transformations.