Data Engineer

Role & responsibilities

• Design, develop, and maintain

  scalable data pipelines

  and

  ETL/ELT workflows

  using

  Databricks, Google BigQuery, SQL, and cloud-native tools

  .
• Build and optimize

  batch and streaming data pipelines

  to support analytics, reporting, and business intelligence use cases.
• Collaborate with

  business stakeholders, product teams, analytics engineers, and data analysts

  to gather requirements and deliver data solutions.
• Develop and manage

  data models, schemas, and transformations

  ensuring data quality, integrity, and consistency.
• Optimize

  SQL queries, partitioning, clustering, and indexing

  for performance and cost efficiency.
• Support

  BI tools and dashboards

  by providing clean, reliable, and analytics-ready datasets.
• Implement and monitor

  data quality checks, validation rules, and error handling

  across pipelines.
• Troubleshoot and resolve

  data pipeline failures, performance issues, and data inconsistencies

  across environments (dev, test, prod).
• Ensure compliance with

  data governance, data security, access controls, and privacy standards

  .
• Work directly with

  clients and external stakeholders

  to gather requirements, present deliverables, and manage expectations.

Preferred candidate profile

• 48 years of experience

  as a

  Data Engineer, Analytics Engineer, or ETL Developer

  .
• Advanced proficiency in

  SQL

  (complex queries, window functions, optimization, performance tuning).
• Strong hands-on experience with

  Google BigQuery

  (partitioning, clustering, cost optimization).
• Experience building data pipelines using

  Databricks

  (Apache Spark, Delta Lake).
• Solid understanding of

  ETL/ELT architecture, data warehousing, dimensional modeling, and star/snowflake schemas

  .
• Experience with

  Python and/or Scala

  for data processing and automation.
• Familiarity with

  cloud platforms

  such as

  Google Cloud Platform (GCP), Azure, or AWS

  .
• Experience with

  pipeline orchestration tools

  (Airflow, Databricks Workflows, or similar) preferred.
• Knowledge of

  data governance, security, IAM, and compliance frameworks

  is a plus.
• Strong

  client-facing, communication, and problem-solving skills

  .
• Ability to work independently in a

  hybrid or remote work environment

  .