10 days ago
Pune, IndiaStaff+
Responsibilities
- Lead end-to-end delivery of enterprise-scale data engineering solutions across multiple clients, technology stacks, and cloud environments.
- Architect and implement scalable data platforms using AWS, Azure, Databricks, Snowflake, and related technologies.
- Design, develop, and optimize ETL/ELT pipelines for structured and unstructured data integration and processing.
- Build modern data architectures including data lakes, data warehouses, lakehouses, and cloud-native platforms.
- Own project lifecycles from requirements gathering and solution architecture through development, deployment, optimization, and production support.
- Engage clients and senior stakeholders to define technical roadmaps and translate business requirements into scalable solutions.
- Establish practices for data modeling, orchestration, quality, governance, security, access controls, and performance optimization.
- Use Python, SQL, and distributed processing frameworks to develop reliable data transformation solutions.
- Collaborate with Business Intelligence, Data Science, and Data Platform teams to support analytics, reporting, and advanced analytics.
- Drive cloud cost optimization, automation, platform performance, and operational efficiency.
- Build and scale data engineering teams, define delivery frameworks, and mentor technical resources.
- Support solutioning, technical proposals, client presentations, effort estimation, and pre-sales discussions.
- Identify opportunities to modernize legacy platforms and migrate workloads to cloud-based architectures.
Requirements
- 8–11 years of experience in data engineering, cloud data platforms, and data architecture.
- 3–4 years of experience in a team management role.
- Strong experience in Life Sciences or Pharma, including relevant data landscapes, business processes, and analytics requirements.
- Proven experience with direct client engagement, stakeholder management, executive communication, and consulting or client-facing delivery.
- Experience architecting and delivering enterprise-scale data engineering solutions from the ground up.
- Strong hands-on experience with AWS or Microsoft Azure and their data engineering and storage services.
- Extensive experience with Databricks and/or Snowflake, including data processing, transformation, and optimization.
- Strong programming skills in Python and SQL.
- Experience designing, developing, and optimizing large-scale ETL/ELT pipelines.
- Strong understanding of data warehousing, data lakes, lakehouse architecture, data modeling, and distributed data processing.
- Experience with workflow orchestration and scheduling tools such as Azure Data Factory, AWS Glue, Airflow, or equivalent technologies.
- Experience building and scaling data engineering teams, defining technical standards, and establishing delivery frameworks.
- Understanding of data governance, data quality, data security, access controls, and enterprise data management.
- Ability to evaluate technical trade-offs, troubleshoot complex data engineering challenges, and translate ambiguous requirements into scalable architectures.
- Strong analytical, problem-solving, communication, and presentation skills for technical and business audiences.
- Experience supporting solution architecture, technical proposals, effort estimation, and pre-sales activities.
- Exposure to reporting tools such as Power BI or Tableau is preferred.
