Technical Delivery Team Manager; Data engineering

Role :
Technical Delivery Team Manager (Data engineering)

Position type :
Fulltime

We are seeking a results-driven Technical Delivery Manager (TDM) to own end-to-end delivery for a multi-track Data Engineering program in the Retail domain
. This role is intended for a senior data engineering leader who combines deep technical fluency with proven delivery leadership—driving predictable execution, engineering discipline, and continuous improvement.

You will lead a 20 person delivery team (cross-geo), with onsite/NA team members reporting directly and strong coordination across matrix/offshore teams. The role is highly stakeholder-facing, partnering with customer Directors, Tech Managers, Program/Project Managers, Product Management, BSAs, Engineers, and Architects
.

This role is not expected to be the primary solution architect for every area, but is expected to influence architecture and engineering outcomes through design reviews, technical governance, and partnership with architects/tech leads.

• Own delivery across multiple tracks: scope, roadmap, milestones, dependencies, budget alignment, RAID, and change control.
• Establish and run governance cadence: weekly status, executive readouts, risk reviews, planning alignment, and steering updates.
• Drive delivery predictability and operational rigor through clear plans, measurable outcomes, and disciplined execution.
• Act as the primary delivery interface for customer stakeholders (Directors, Tech Managers, PgMs/PjMs, Product team, BSAs, Engineers, Architects).
• Translate business priorities into actionable plans with clear acceptance criteria, sequencing, and release milestones.
• Communicate proactively on progress, tradeoffs, risks, mitigations, and decisions required; manage escalations and align on next steps.

• Lead delivery across ingestion, streaming, transformation, orchestration, and data product enablement using:
• Airflow for orchestration, scheduling, dependency management, and operational SLAs
• Kafka for event/stream processing patterns and reliability considerations
• Databricks/Spark for processing and pipelines
• Snowflake for warehousing/analytics, performance, and cost considerations
• Postgres for operational data sources / integrations
• Influence solution quality through technical governance
  : design reviews, coding standards, data modeling standards, performance practices, and reusable patterns.
• Ensure production readiness: monitoring/alerting, runbooks, incident response, root-cause analysis, and preventative actions across tracks.
• Lead and motivate a ~20-person team; directly manage NA/onsite reports and influence matrix/offshore teams to deliver outcomes.
• Build a culture of ownership, accountability, collaboration, and continuous improvement.
• Coach leads and scrum masters on execution discipline, stakeholder communication, and delivery predictability.

• Drive efficiency via automation and AI-assisted workflows (e.g., AI-assisted code review, test/data validation generation, documentation/runbooks acceleration, incident triage support).
• Implement Lean/Kaizen practices to reduce cycle time, defect leakage, operational toil, and cost.
• Establish delivery and operational metrics to identify bottlenecks and continuously improve throughput, quality, and reliability.

• 10+ years in technology, with 5+ years in data engineering leadership and/or technical delivery leadership for complex programs (services/consulting experience strongly preferred).
• Proven success delivering multi-track programs with complex dependencies and senior stakeholder engagement.
• Deep experience in data engineering delivery including ETL/ELT, orchestration, data modeling, data quality, observability/monitoring, and production support.
• Technical fluency sufficient to lead engineering outcomes and challenge decisions (not required to be the primary architect for all areas), including:
• SQL (advanced) and Python fundamentals
• Snowflake and/or Databricks/Spark delivery experience (one can be primary; the other must be strong working knowledge)
• Airflow concepts (DAG patterns, scheduling, SLAs, failure handling, operationalization)
• Kafka concepts (producer/consumer patterns, reliability considerations, event-driven delivery)
• Postgres fundamentals (query design/performance basics; source/integration patterns)
• Strong Agile delivery experience (Scrum/Kanban) and tools (Jira/Confluence or equivalent).
• Excellent communication skills—able to align technical and non-technical audiences and drive decisions.