Senior Data Pipeline Engineer

1 day ago

Hyderabad, Telangana, India beBeeDataArchitect Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Job Title: Data Architect

We are seeking a skilled Data Architect to design and develop efficient data pipelines, applications, and infrastructure to support business growth.

  • Coding & Development: Build, test, and optimize data processing code to ensure scalability; define coding standards, templates, and checklists; perform peer code reviews.
  • Documentation: Create/review design, architecture, and process documentation; develop mapping sheets, test cases, and infra cost estimations.
  • Configuration & Testing: Define configuration management plans and ensure compliance; design/review unit tests, test plans, and support QA teams.
  • Domain & Business Relevance: Guide engineers on domain-specific data use cases; understand customer domain and add business value; complete relevant domain certifications.
  • Project & Delivery: Support project planning and sprint execution; manage delivery of assigned modules; contribute to design/architecture (HLD, LLD, SAD).
  • Defect & Release Management: Conduct root cause analysis (RCA) and reduce recurring issues; execute and monitor release processes.
  • Customer Interaction: Clarify requirements and provide technical guidance; present design options and conduct product demos; collaborate with customer architects.
  • Team Management: Set goals, provide feedback, and enable upskilling; mentor team members and identify retention risks.
Key Responsibilities:
  1. Familiarity with ETL Tools: Informatica, Apache Airflow, AWS Glue, Dataproc, Azure ADF, Talend.
  2. Experience with Cloud Platforms: AWS, Azure, GCP (Glue, BigQuery, Dataflow, ADLS, etc.).
  3. Proficiency in Data Warehousing: Snowflake, BigQuery, Lakehouse, Delta Lake.
  4. Strong analytical and problem-solving ability.
  5. Ability to explain technical concepts to business stakeholders.
  6. Mentorship and leadership skills.

This role requires strong expertise in developing and optimizing data pipelines, including coding, testing, and transforming/joining data from multiple sources. Expertise in data warehousing solutions is essential, along with the ability to optimize performance, calculate processing costs, and manage infrastructure needs. A solid understanding of DevOps practices and data infrastructure is also required.

Measures of Outcomes:
  1. Adherence to engineering processes, standards, and SLAs.
  2. On-time delivery and reduction in post-production defects.
  3. Faster turnaround for production issues.
  4. Improvement in pipeline efficiency (lower resource consumption, faster runtimes).
  5. Compliance with data security and governance.
  6. Completion of certifications and mandatory training.
  7. Improved detection and resolution times for pipeline failures.

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