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 Important Notice We have been alerted to the existence of fraudulent messages asking job seekers to set up payment to cover various costs associated with establishing employment at Accenture. 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