Technical Project Manager

• Data engineering


• AIML

Key
Responsibilities
1. Technical Delivery & Lifecycle Management

Orchestrate Data & AI Projects:
Manage the end-to-end lifecycle of data projects, from raw data ingestion(ELT/ETL) to AI Model deployment and monitoring.

Technical Literacy: actively
participate in architectural discussions. You must understand the implicationsof technical choices (e.g., Batch vs.
Real-time processing, Cloud costs of
GPU clusters) to make informed planning decisions.

Practice Alignment: Collaborate with Practice Leadership (AI/ML, Data Eng)
to ensure your team adheres to the organizations coding standards, securityprotocols, and architectural best practices.

Ambiguity Management: Specifically for
AI/ML, manage the uncertainty of R&D phases (e.g., when a model fails toconverge) and translate that into predictable timelines for the client.

2. Client
Engagement & Consulting

Expectation Management: Act as the
primary liaison between the technical team and the client stakeholders.Translate "Technical Debt" and "Model Drift" into businessrisks and requirements.

Scope Governance: rigorously manage
scope creep. Identify when client requests deviate from the SOW and handleChange Requests (CRs) commercially.

Client Education: Guide clients who are
new to AI/Data on the prerequisites of the project (e.g., Data Quality, AccessRights, Infrastructure needs).

3. Operational
Excellence (Engagement Models)

For T&M Engagements: Monitor team
utilization, burn rates, and velocity. Ensure the client sees visible value inevery sprint to justify the ongoing investment.

For BOT (Build-Operate-Transfer):

o
Build:
Ensure documentation is created during
development, not after.

o
Operate:
Establish SLAs and Runbooks for the solution.

o
Transfer:
Lead the knowledge transfer sessions to upskill the client s internal team forthe eventual handover.

4. Agile
Project Management

Run
Scrum ceremonies (Stand-ups, Planning, Retro, Demos) tailored for Data/AIteams.

Maintain
the backlog in Jira/Azure DevOps, ensuring User Stories meet the"Definition of Ready" (DoR) regarding data availability beforedevelopment starts.

Remove
impediments, specifically those unique to data (e.g., "Waiting forfirewall access to Source Database").

Required Skills
& Qualifications Technical Knowledge (Must Have)

Data Engineering: Understanding of
Modern Data Stack (Snowflake, Databricks, BigQuery, Redshift), ETL/ELT tools(dbt, Airflow, ADF), and Data Modeling concepts.

AI/ML Awareness: Understanding of the
Data Science lifecycle (Training, Testing, Validation) and MLOps concepts(Model Registry, Drift Monitoring).

Cloud Native: Familiarity with AWS,
Azure, or GCP ecosystems.

Coding Literacy: Ability to
read/understand SQL and Python (You wont code daily, but you must understandwhat your team is building).

Project Management
Competencies

Methodology: Certified Scrum Master
(CSM), SAFe Agilist, or PMP.

Experience: 5+ years in Project
Management with at least 3 years focused on Data, BI, or Analytics projects.

Financials: Experience managing project
P&L, resource forecasting, and margin analysis.

Soft Skills

Structured Thinking: Ability to break
down complex abstract data problems into executable tasks.

Diplomacy: Experience navigating matrix
organizations and managing difficult client conversations regarding timelinesand budget.