Technical Lead

Role & responsibilities

• Lead the

  end-to-end technical delivery

  of

  data engineering, data warehousing, and analytics solutions

  , from

  requirements discovery

  to

  production deployment

  .
• Partner closely with

  business stakeholders and clients

  to

  understand, analyze, and translate business requirements

  into robust data architectures and technical designs.
• Own and drive the

  data modeling strategy

  , including

  conceptual, logical, and physical models

  ,

  dimensional modeling

  , and

  KPI/metric definitions

  .
• Architect, design, and optimize

  modern data platforms

  using

  Databricks, Snowflake, and cloud-native services

  .
• Design, build, review, and optimize

  ETL/ELT pipelines

  , data ingestion frameworks, and transformation logic for large-scale data processing.
• Develop and optimize

  SQL-based transformations and analytical datasets

  , ensuring performance, scalability, and maintainability.
• Lead the creation of

  analytics-ready datasets, dashboards, and reporting layers

  aligned to business objectives.
• Perform

  code reviews and design reviews

  , establishing and enforcing best practices for

  data quality, testing, documentation, and governance

  .
• Troubleshoot and resolve

  complex data pipeline failures, performance bottlenecks, and data quality issues

  across environments.
• Define and implement

  data governance, security, lineage, and access control standards

  .
• Influence and drive

  technical decision-making

  , including architecture patterns, modeling approaches, tooling selection, and optimization strategies.
• Act as a

  technical advisor and mentor

  , guiding engineers and raising overall engineering and data modeling standards.
• For Technical Leadoriented candidates: serve as a

  client-facing technical lead

  , owning delivery plans, risks, and execution.
• For Principal-level candidates: provide

  organization-wide technical leadership

  , shaping platform strategy and long-term architecture.

Preferred candidate profile

• Bachelors degree in

  Computer Science, Data Engineering, Information Systems

  , or a related field.

• 6–10+ years

  of hands-on experience in

  Data Engineering, Data Warehousing, or Analytics Engineering

  .
• Proven experience in

  requirements analysis

  , stakeholder collaboration, and translating business needs into technical solutions.
• Strong expertise in

  data modeling

  , including

  dimensional modeling, star/snowflake schemas, and analytics layer design

  .
• Hands-on experience with

  Databricks (Apache Spark, Delta Lake)

  and/or

  Snowflake

  .
• Advanced proficiency in

  SQL

  for transformations, performance tuning, validation, and troubleshooting.
• Experience with

  Python and/or Scala

  for data processing and pipeline development.
• Solid understanding of

  cloud data architectures

  (GCP, AWS, or Azure).
• Experience implementing

  ETL/ELT pipelines, orchestration, CI/CD, and monitoring

  for data platforms.
• Strong understanding of

  data governance, security, and compliance

  in cloud environments.
• Excellent

  technical communication, problem-solving, and mentoring skills

  .
• Ability to balance

  hands-on engineering

  ,

  technical leadership

  , and

  stakeholder engagement

  .