About the role
We are hiring a Program Manager to own end-to-end delivery of large, complex Data & AI programs for enterprise customers, with a focus on US telecom accounts. You will lead a team of about 30, run large-scale migration programs onto the Databricks platform, and manage data and AI solutions around telecom billing and invoicing.
This is a customer-facing leadership role. You will be the primary point of contact for senior customer stakeholders and will steer delivery across a multi-party ecosystem of customer IT, business teams, product vendors and partners.
Key responsibilities
Program delivery
• Own scope, schedule, budget, quality and risk for multi-workstream Data & AI programs of enterprise scale.
• Plan and run large-scale migrations from legacy data platforms (on-prem warehouses, Hadoop, legacy ETL) to the Databricks Lakehouse, covering assessment, wave planning, code conversion, data validation, cutover, reconciliation and legacy decommissioning.
• Set up program governance: steering committees, RAID logs, status reporting, change control and escalation paths.
• Track delivery health with clear metrics (milestones, burn, defects, SLAs) and act early on slippage.
Telecom billing and invoicing
• Lead data and AI initiatives across the billing lifecycle: usage/rating data, invoice generation, revenue assurance, dispute and adjustment analytics.
• Translate billing and invoicing business processes into data requirements, data models and reconciliation controls.
• Ensure data accuracy and auditability for financially sensitive billing data.
Customer management
• Act as the primary delivery contact for US customer leadership (Director, VP and CXO level).
• Manage a complex ecosystem of customer IT and business teams, third-party vendors, platform partners and other SIs.
• Run business reviews (weekly, monthly, QBR), manage expectations and handle escalations with composure.
• Spot account growth opportunities and support proposals, SOWs and change requests.
People leadership
• Lead a team of about 30 across data engineering, ML, architecture, QA and analysis, working across US and India time zones.
• Drive hiring, onboarding, allocation, performance and career growth for the team.
• Build a delivery culture of ownership, quality and clear communication.
Must-have qualifications
• 12–18 years in IT services or product delivery, with 5+ years managing large Data & AI programs end to end.
• Proven delivery of large, complex, enterprise-grade data programs (multi-workstream, multi-million-dollar, high visibility).
• Has led at least one large-scale, enterprise migration to the Databricks platform end to end (multi-TB to PB data, hundreds of pipelines, phased cutover with parallel runs).
• Domain experience in telecom billing and invoicing processes (rating, invoicing, revenue assurance, billing data reconciliation).
• Has led teams of 25–35 people, including onsite–offshore delivery models.
• Has managed complex customer ecosystems with multiple vendors and stakeholders, preferably for US customers.
• Excellent articulation and communication, written and verbal; confident presenting to senior customer leadership.
• Sound grasp of modern data architecture: lakehouse, ETL/ELT, data governance, cloud platforms (AWS, Azure or GCP).
• Working knowledge of AI/ML and GenAI delivery (MLOps, model deployment, use-case scoping).
• Strong command of delivery frameworks (Agile/Scrum, SAFe or hybrid) and program financials.
• Bachelor's degree in Engineering or Computer Science; MBA a plus.
Technology skills
The candidate need not code daily, but must be able to review designs, challenge estimates and guide architects on the areas below.
|
Area |
Skills expected |
|
Databricks platform |
Lakehouse architecture, Delta Lake, Unity Catalog, Databricks SQL and SQL Warehouses, Workflows/Jobs, Delta Live Tables, Photon, cluster and cost management |
|
Migration sources |
Teradata, Oracle, Netezza, SQL Server, Hadoop/Cloudera (Hive, Spark), Informatica, DataStage, SSIS, SAS |
|
Migration approach |
Discovery and inventory, lift-and-shift vs re-engineer decisions, SQL/ETL code conversion, data validation and reconciliation, parallel runs, cutover and rollback planning |
|
Data engineering |
Apache Spark (PySpark, Spark SQL), batch and streaming (Structured Streaming, Kafka, Auto Loader), medallion architecture |
|
Governance and security |
Unity Catalog access control, data lineage, data quality frameworks, PII handling for billing and customer data |
|
AI/ML on Databricks |
MLflow, Feature Store, Model Serving, Mosaic AI / GenAI use cases |
|
Cloud |
Databricks on AWS, Azure or GCP; object storage, networking and IAM basics |
|
DevOps and FinOps |
CI/CD with Databricks Asset Bundles, Git, Terraform; DBU cost monitoring and optimisation |
|
Certifications (preferred) |
Databricks Certified Data Engineer (Associate/Professional) or Databricks Lakehouse Fundamentals |
Good to have
• Certifications: PMP, PgMP, SAFe (RTE/SPC), or Databricks / cloud certifications.
• Exposure to telecom BSS/OSS stacks and billing platforms (e.g., Amdocs, Netcracker, Ericsson, CSG).
• Experience with Databricks migration accelerators and code-conversion tools (e.g., Lakebridge / BladeBridge or partner-built converters).
• Prior onsite stint or extensive travel experience with US customers.
• Experience in pre-sales, solutioning or account mining for data programs.