← Data Engineering — M5

Goal templates — Data Engineering — M5

Data & Database Engineering · Data Engineering · M5 — Senior Director

These are canon-derived frames, not advice: every line is either verbatim JobFrame canon text or a fixed template wrapping it. ⟨target⟩ / ⟨baseline⟩ / ⟨date⟩ are placeholders for the manager to fill in. Nothing here is generated by AI — rows are omitted, never invented, when the canon lacks the underlying field.

SMART goals

One row per canon core output / responsibility this level owns.

JFM responsibility (M5)

Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud.

Specific
Deliver: "Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
Relevant
Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
Time-bound
⟨date⟩

JFM responsibility (M5)

Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide.

Specific
Deliver: "Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
Relevant
Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
Time-bound
⟨date⟩

JFM responsibility (M5)

Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit.

Specific
Deliver: "Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
Relevant
Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
Time-bound
⟨date⟩

JFM responsibility (M5)

Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt.

Specific
Deliver: "Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
Relevant
Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
Time-bound
⟨date⟩

JFM responsibility (M5)

Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org.

Specific
Deliver: "Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org."
Measurable
Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable
Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
Relevant
Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
Time-bound
⟨date⟩
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1. Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud.  [source: JFM responsibility (M5)]
   Specific:    Deliver: "Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
   Time-bound:  ⟨date⟩

2. Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide.  [source: JFM responsibility (M5)]
   Specific:    Deliver: "Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
   Time-bound:  ⟨date⟩

3. Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit.  [source: JFM responsibility (M5)]
   Specific:    Deliver: "Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
   Time-bound:  ⟨date⟩

4. Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt.  [source: JFM responsibility (M5)]
   Specific:    Deliver: "Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
   Time-bound:  ⟨date⟩

5. Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org.  [source: JFM responsibility (M5)]
   Specific:    Deliver: "Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M5 — Senior Director.
   Time-bound:  ⟨date⟩

OKRs

Objectives from this level's core outputs; key results only where a real dimension or capability backs them.

JFM responsibility (M5)

Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud."
  • Evidence at this level's scope bar: "Multiple functions or a large department" — ⟨target⟩ by ⟨date⟩

JFM responsibility (M5)

Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide."
  • Evidence at this level's autonomy bar: "Owns multi-year strategy for the area" — ⟨target⟩ by ⟨date⟩

JFM responsibility (M5)

Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit."
  • Evidence at this level's complexity bar: "Org-level trade-offs and investment" — ⟨target⟩ by ⟨date⟩

JFM responsibility (M5)

Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt."
  • Evidence at this level's impact bar: "Multi-function results" — ⟨target⟩ by ⟨date⟩

JFM responsibility (M5)

Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org.

  • From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org."
  • Evidence at this level's decision rights bar: "Owns investment and org design across functions" — ⟨target⟩ by ⟨date⟩
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Objective 1: Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud.  [source: JFM responsibility (M5)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud."
  KR2. Evidence at this level's scope bar: "Multiple functions or a large department" — ⟨target⟩ by ⟨date⟩

Objective 2: Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide.  [source: JFM responsibility (M5)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide."
  KR2. Evidence at this level's autonomy bar: "Owns multi-year strategy for the area" — ⟨target⟩ by ⟨date⟩

Objective 3: Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit.  [source: JFM responsibility (M5)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit."
  KR2. Evidence at this level's complexity bar: "Org-level trade-offs and investment" — ⟨target⟩ by ⟨date⟩

Objective 4: Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt.  [source: JFM responsibility (M5)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt."
  KR2. Evidence at this level's impact bar: "Multi-function results" — ⟨target⟩ by ⟨date⟩

Objective 5: Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org.  [source: JFM responsibility (M5)]
  KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org."
  KR2. Evidence at this level's decision rights bar: "Owns investment and org design across functions" — ⟨target⟩ by ⟨date⟩

MBO areas

Key result areas from this level's responsibilities, each with a standard grounded in the canon leveling rubric where one exists.

AreaStandardTargetDue
Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud.Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."⟨target⟩⟨date⟩
Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide.Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."⟨target⟩⟨date⟩
Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit.Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."⟨target⟩⟨date⟩
Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt.Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."⟨target⟩⟨date⟩
Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org.Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."⟨target⟩⟨date⟩
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1. Area: Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud.  [source: JFM responsibility (M5) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."
   Target:   ⟨target⟩   Due: ⟨date⟩

2. Area: Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide.  [source: JFM responsibility (M5) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."
   Target:   ⟨target⟩   Due: ⟨date⟩

3. Area: Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit.  [source: JFM responsibility (M5) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."
   Target:   ⟨target⟩   Due: ⟨date⟩

4. Area: Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt.  [source: JFM responsibility (M5) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."
   Target:   ⟨target⟩   Due: ⟨date⟩

5. Area: Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org.  [source: JFM responsibility (M5) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards."
   Target:   ⟨target⟩   Due: ⟨date⟩

Scorecard

Only perspectives with real canon backing are shown — no Financial or Customer perspective, since nothing in the canon grounds business-financial or customer measures for a role alone.

Internal process

  • "Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud."→ ⟨target⟩ by ⟨date⟩
  • "Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide."→ ⟨target⟩ by ⟨date⟩
  • "Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit."→ ⟨target⟩ by ⟨date⟩
  • "Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt."→ ⟨target⟩ by ⟨date⟩
  • "Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org."→ ⟨target⟩ by ⟨date⟩

Role calibration

  • Meets the scope bar: "Multiple functions or a large department"→ ⟨target⟩ by ⟨date⟩
  • Meets the autonomy bar: "Owns multi-year strategy for the area"→ ⟨target⟩ by ⟨date⟩
  • Meets the complexity bar: "Org-level trade-offs and investment"→ ⟨target⟩ by ⟨date⟩
  • Meets the impact bar: "Multi-function results"→ ⟨target⟩ by ⟨date⟩
  • Meets the decision rights bar: "Owns investment and org design across functions"→ ⟨target⟩ by ⟨date⟩
  • Meets the leadership bar: "Leads directors and managers"→ ⟨target⟩ by ⟨date⟩
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Internal process
  - "Directs the data engineering organization through subordinate managers, owning the division-wide data platform strategy, operating model, and consolidated budget across every team and cloud."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M5)]
  - "Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M5)]
  - "Influences executives and major internal/external stakeholders on platform investments, multi-year build-vs-buy decisions, and long-term technical direction for the business unit."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M5)]
  - "Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M5)]
  - "Sets the talent strategy and second-level management structure for the department, owning leadership development, succession, and the long-term technical direction of the engineering org."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M5)]

Role calibration
  - Meets the scope bar: "Multiple functions or a large department"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Scope)]
  - Meets the autonomy bar: "Owns multi-year strategy for the area"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Autonomy)]
  - Meets the complexity bar: "Org-level trade-offs and investment"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Complexity)]
  - Meets the impact bar: "Multi-function results"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Impact)]
  - Meets the decision rights bar: "Owns investment and org design across functions"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Decision rights)]
  - Meets the leadership bar: "Leads directors and managers"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Leadership)]