[India] - Senior Engagement Lead - Data Engineer

About ProcDNA

ProcDNA is a global consulting firm. We fuse design thinking with cutting-edge technology to create game-changing Commercial Analytics and Technology solutions for our clients. We're a passionate team of 475+ across 9 offices, all growing and learning together since our launch during the pandemic. Here, you won't be stuck in a cubicle - you'll be out in the open water, shaping the future with brilliant minds. At ProcDNA, innovation isn't just encouraged; it's ingrained in our DNA. Ready to join our epic growth journey?

What We Are Looking For

A seasoned Data Engineering expert who can drive the adoption of modern data platforms and provide technical thought leadership to clients across cloud data engineering, data architecture, and enterprise data management.

This role requires deep expertise in building scalable, high-performance data engineering solutions using cloud technologies, modern data platforms, and large-scale data processing frameworks. The ideal candidate will have strong experience in Life Sciences / Pharma, the ability to lead complex client engagements, and a proven track record of building high-performing teams and delivering measurable business impact in a fast-paced, global consulting environment.

The Senior Engagement Lead will partner with clients and internal leadership to shape data engineering strategies, architect scalable solutions, strengthen technical capabilities, and drive delivery excellence across multiple engagements.

What You'll Do

- Lead end-to-end delivery of enterprise-scale data engineering solutions across multiple clients, technology stacks, and cloud environments.
- Architect and implement scalable, secure, and high-performance data platforms using AWS, Azure, Databricks, Snowflake, and related technologies.
- Design and optimize robust ETL/ELT pipelines to ingest, transform, process, and integrate data from multiple structured and unstructured sources.
- Drive the development of modern data architectures, including data lakes, data warehouses, lakehouses, and cloud-native data platforms.
- Own the complete project lifecycle, from requirements gathering and solution architecture to development, deployment, performance optimization, and production support.
- Engage directly with clients and senior stakeholders to understand business challenges, define technical roadmaps, and translate requirements into scalable data engineering solutions.
- Establish best practices for data modeling, pipeline orchestration, data quality, data governance, security, and performance optimization.
- Leverage Python, SQL, and distributed data processing frameworks to build reliable, reusable, and efficient data transformation solutions.
- Collaborate with Business Intelligence, Data Science, and Data Platform teams to enable seamless data integration and support downstream analytics, reporting, and advanced analytics use cases.
- Drive cloud cost optimization, platform performance, automation, and operational efficiency across data engineering implementations.
- Build and scale data engineering teams, establish delivery frameworks, and mentor technical resources to strengthen organizational capabilities.
- Support business development initiatives, including solutioning, technical proposals, client presentations, effort estimation, and pre-sales discussions.
- Identify opportunities to modernize legacy data platforms and migrate workloads to scalable cloud-based architectures.
- Drive continuous improvement through reusable accelerators, engineering standards, automation, and emerging data technologies.

Must Have

- 8–11 years of experience in Data Engineering, Cloud Data Platforms, and Data Architecture, with 3–4 years in a team management role.
- Strong domain experience in Life Sciences / Pharma, with an understanding of the data landscape, business processes, and analytics requirements within the industry.
- Proven experience in direct client engagement, stakeholder management, and executive communication in a consulting or client-facing delivery environment.
- Demonstrated ability to architect and deliver enterprise-scale data engineering solutions from the ground up.
- Strong hands-on expertise in cloud platforms such as AWS or Microsoft Azure , including their data engineering and storage services.
- Extensive experience with modern data platforms such as Databricks and/or Snowflake , including data processing, transformation, and optimization.
- Strong programming skills in Python and SQL , with experience developing scalable data processing logic and complex data transformations.
- Proven experience designing, developing, and optimizing ETL/ELT pipelines for large-scale data integration and processing.
- Strong understanding of data warehousing, data lakes, lakehouse architecture, data modeling, and distributed data processing concepts.
- Experience with workflow orchestration and pipeline 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 and engineering best practices.
- Strong understanding of data governance, data quality, data security, access controls, and enterprise data management principles.
- Ability to evaluate technical trade-offs, troubleshoot complex data engineering challenges, and translate ambiguous business requirements into scalable technical architectures.
- Strong analytical, problem-solving, communication, and presentation skills, with the ability to communicate complex technical concepts to both technical and business stakeholders.
- Experience supporting solution architecture, technical proposals, effort estimation, and pre-sales activities.
- Some exposure to reporting tools such as Power BI or Tableau is preferrable.