Lead Data Engineer

Job Title - Lead Data Engineer – Specialist - ACS SONG 

Management Level: Level 9 - Specialist

Location: Kochi, Coimbatore, Trivandrum

Must have skills:  GCP services, BigQuery, Python, and SQL. 

Good to have skills: BI and Analytical Tool          

Experience: 5 -8 years of experience is required

Educational Qualification: Graduation

Job Summary

We are seeking a Lead Data Engineer specializing in Google Cloud Platform (GCP) and BigQuery with 5+ years of experience in data engineering, cloud-based data platforms, data pipelines, SQL development, and large-scale data processing. The role will focus on designing, developing, and optimizing scalable data pipelines and analytics solutions using BigQuery and other GCP data services. 

Roles and Responsibilities

• The ideal candidate should have strong hands-on experience with BigQuery, Python, SQL, Cloud Storage, Dataflow, Pub/Sub, Dataproc, and Cloud Composer, along with a solid understanding of ETL/ELT, data warehouse, data lake, and modern cloud data architecture principles. 
•  This position requires the ability to independently design and deliver production-grade data solutions, optimize performance and cost, troubleshoot complex data issues, and collaborate closely with architects, data scientists, analysts, application teams, and business stakeholders. 
• Design, develop, and maintain scalable data pipelines and data processing solutions using GCP services, BigQuery, Python, and SQL. 
• Build reliable and high-performance data transformation and integration workflows supporting reporting, analytics, machine learning, and downstream business applications. 
• Drive improvements in data quality, pipeline performance, BigQuery optimization, cost efficiency, monitoring, security, and production stability. 
• Provide technical guidance and contribute to the adoption of modern GCP data engineering architecture, standards, and development best practices. 
• Design, develop, and maintain batch and real-time data pipelines using BigQuery, Cloud Storage, Dataflow, Pub/Sub, Dataproc, Cloud Composer, Python, and SQL. 
• Develop ETL/ELT solutions for ingesting, transforming, and integrating structured, semi-structured, and unstructured data from multiple source systems. 
• Design and maintain scalable BigQuery datasets, tables, views, materialized views, partitioning, clustering, and data models for analytics and reporting workloads. 
• Optimize BigQuery SQL queries, storage structures, data processing workloads, and pipeline architecture to improve performance and control cloud consumption costs. 
• Build and maintain batch and streaming data processing solutions using services such as Dataflow, Pub/Sub, Dataproc, and BigQuery. 
• Design curated data layers, reusable datasets, and data models supporting enterprise analytics, BI, machine learning, and downstream applications. 
• Implement data validation, reconciliation, data quality checks, error handling, lineage, monitoring, alerting, and operational controls. 
• Orchestrate data pipelines and workflows using Cloud Composer / Apache Airflow, Workflows, or equivalent orchestration technologies. 
• Collaborate with solution architects, data architects, data scientists, analysts, application teams, and business stakeholders to translate requirements into scalable technical solutions. 
• ·  Follow and promote engineering best practices covering Git-based development, automated testing, CI/CD, Infrastructure as Code, documentation, monitoring, security, and production support. 

Professional and Technical Skills

• 5+ years of experience in data engineering, data warehousing, ETL/ELT development, SQL development, or large-scale data processing. 
• Strong hands-on experience designing and implementing data solutions on Google Cloud Platform (GCP). 
• Strong production experience with Google BigQuery for enterprise data warehousing, analytics, and large-scale data processing. 
• Experience designing and supporting production-grade batch and/or streaming data pipelines. 
• Experience working with large datasets and building scalable, reliable, and high-performance cloud data solutions. 
• Strong expertise in Google BigQuery, including SQL development, partitioning, clustering, materialized views, optimization techniques, query execution analysis, and cost management. 
• Strong programming experience in Python for data processing, pipeline development, automation, and integration. 
• Advanced SQL skills, including complex joins, aggregations, CTEs, window functions, analytical queries, query optimization, and troubleshooting. 
• Hands-on experience with GCP data services such as: 
  • BigQuery 
  • Google Cloud Storage 
  • Dataflow 
  • Pub/Sub 
  • Dataproc 
  • Cloud Composer / Apache Airflow 
• Good understanding of batch processing, real-time/streaming architecture, ETL/ELT patterns, data lakes, data warehouses, and modern cloud data platforms. 
• Experience designing dimensional, analytical, and curated data models supporting reporting, BI, analytics, and machine learning use cases. 
• Good understanding of BigQuery performance and cost optimization, including partition pruning, clustering, query optimization, slot utilization, storage optimization, and workload management. 
• Experience with Dataform, dbt, or similar SQL-based data transformation frameworks is preferred. 
• Experience with Cloud Functions, Cloud Run, Workflows, or other serverless GCP services is an advantage. 
• Understanding of GCP security concepts including IAM, service accounts, encryption, Secret Manager, VPC Service Controls, and data access management. 
• Familiarity with Git, CI/CD pipelines, Infrastructure as Code such as Terraform, automated testing, logging, monitoring, and deployment practices. 
• Experience with Cloud Monitoring, Cloud Logging, or equivalent observability tools for production data pipelines is preferred. 
• Exposure to BI and analytics tools such as Looker, Looker Studio, Power BI, or Tableau is an added advantage. 
• Strong analytical, troubleshooting, and problem-solving skills with the ability to independently investigate complex data and pipeline issues. 
• Strong communication skills with the ability to work effectively with technical teams, architects, business stakeholders, and cross-functional teams. 
• Ability to translate business and functional requirements into scalable and maintainable technical data solutions. 
• Ability to provide technical guidance, perform code reviews, establish development standards, and support junior engineers. 
• Strong ownership mindset with a focus on data quality, scalability, performance, security, cost efficiency, reliability, and timely delivery. 
• Ability to work effectively in distributed and agile delivery teams and manage multiple priorities in a fast-paced environment. 

 

Additional Information       

 

About Our Company | Accenture

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