Principal Data Engineer

You will act as a key member of the consulting team, helping clients to reinvent their corporate finance function by leveraging advanced analytics. You will be working closely and directly with senior stakeholders of the clients, designing and implementing data strategy in the finance space, which includes multiple use cases, viz., controllership, FP& A, and GPO. You will be responsible for developing technical solutions to deliver scalable analytical solutions leveraging cloud and big data technologies. You will also collaborate with business consultants and product owners to design and implement technical solutions. Communication and organizational skills are key for this position.

Responsibilities:

• Design and drive end-to-end data and analytics solution architecture from concept to delivery on Google Cloud Platform and Azure.
• Design, develop, and support conceptual, logical, and physical data models for advanced analytics and ML-driven solutions.
• Ensure integration of industry-accepted data architecture principles, standards, guidelines, and concepts with other domains, along with coordinated roll-out and adoption strategies.
• Drive the design, sizing, provisioning, and setup of GCP and Azure environments and related services such as BigQuery, Dataflow, and Cloud Storage.
• Provide mentoring and guidance on GCP and Azure-based data architecture to engineering, analytics, and business teams.
• Review solution requirements and architecture for appropriate technology selection, efficient resource utilization, and effective integration across systems and technologies.
• Advise on emerging GCP and Azure trends and services, and recommend adoption strategies to maintain a competitive edge.
• Actively participate in pre-sales engagements and PoCs and contribute to publishing thought leadership content.
• Collaborate closely with the founders and leadership team to shape and drive the organization's cloud and data strategy.

Requirements:

• Demonstrated experience in delivering multiple data and analytics solutions on GCP and Azure.
• Hands-on experience with data ingestion, processing, and orchestration tools such as Dataflow, Pub/Sub, Dataproc, Cloud Composer, and Data Fusion.
• Deep expertise with GCP and Azure data warehousing and analytics services, including BigQuery, Cloud Storage, and Looker.
• Familiar with Data Mesh, Data Fabric, and Data Products, Data Contracts, and experience in Data Mesh implementation.
• Strong understanding and practical experience with different data modeling techniques (relational, star, snowflake, Data Vault, etc. ) and working with transactional, time-series, and unstructured datasets.
• Experience with enterprise data management, i. e., data quality, metadata management, data governance, and data observability using GCP and Azure-native or third-party tools.
• Experience in the design and implementation of event-driven architecture and tools like Google Pub/Sub or Kafka will be an added advantage.
• Familiar with AI, GenAI concepts, and Vertex AI.
• Solid grasp of operational dependencies and integration across applications, networks, security, and infrastructure in the cloud, including IAM, VPCs, VPNs, firewall rules, GKE, and service accounts.
• Strong foundation in computer science or software engineering, with expertise in software development methodologies and Dev/Data Ops and CI/CD.
• Practical experience using tools such as Terraform, Cloud Build, GitHub Actions, and scripting via Python, Bash, etc.
• Familiar with the software development lifecycle and cloud-native application development.
• Remains hands-on with technology and stays current with industry trends and GCP and Azure service evolution.
• Demonstrated hands-on experience coding in Python, SQL, and Spark, with the flexibility to pick up new languages or technologies quickly.
• GCP and Azure professional data engineer certifications will be an added advantage.