Goal templates — Data Engineering — M1

Data & Database Engineering · Data Engineering · M1 — Manager (Team Lead)

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 (M1)

Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog.

Specific: Deliver: "Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog."
Measurable: Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable: Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
Relevant: Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
Time-bound: ⟨date⟩

JFM responsibility (M1)

Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team.

Specific: Deliver: "Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team."
Measurable: Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable: Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
Relevant: Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
Time-bound: ⟨date⟩

JFM responsibility (M1)

Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget.

Specific: Deliver: "Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget."
Measurable: Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable: Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
Relevant: Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
Time-bound: ⟨date⟩

JFM responsibility (M1)

Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups.

Specific: Deliver: "Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups."
Measurable: Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable: Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
Relevant: Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
Time-bound: ⟨date⟩

JFM responsibility (M1)

Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management.

Specific: Deliver: "Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management."
Measurable: Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
Achievable: Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
Relevant: Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
Time-bound: ⟨date⟩

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1. Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog.  [source: JFM responsibility (M1)]
   Specific:    Deliver: "Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
   Time-bound:  ⟨date⟩

2. Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team.  [source: JFM responsibility (M1)]
   Specific:    Deliver: "Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
   Time-bound:  ⟨date⟩

3. Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget.  [source: JFM responsibility (M1)]
   Specific:    Deliver: "Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
   Time-bound:  ⟨date⟩

4. Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups.  [source: JFM responsibility (M1)]
   Specific:    Deliver: "Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
   Time-bound:  ⟨date⟩

5. Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management.  [source: JFM responsibility (M1)]
   Specific:    Deliver: "Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management."
   Measurable:  Move the metric this drives from ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩.
   Achievable:  Scoped to this level's jfm complexity/problem-solving rubric: "Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget."
   Relevant:    Advances the Data & Database Engineering · Data Engineering mandate for a M1 — Manager (Team Lead).
   Time-bound:  ⟨date⟩

OKRs

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

JFM responsibility (M1)

From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog."
Evidence at this level's scope bar: "A single team" — ⟨target⟩ by ⟨date⟩

JFM responsibility (M1)

Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team.

From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team."
Evidence at this level's autonomy bar: "Manages within established goals" — ⟨target⟩ by ⟨date⟩

JFM responsibility (M1)

From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget."
Evidence at this level's complexity bar: "Day-to-day delivery and people issues" — ⟨target⟩ by ⟨date⟩

JFM responsibility (M1)

Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups.

From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups."
Evidence at this level's impact bar: "Team output and health" — ⟨target⟩ by ⟨date⟩

JFM responsibility (M1)

Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management.

From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management."
Evidence at this level's decision rights bar: "Owns team execution, hiring input, performance" — ⟨target⟩ by ⟨date⟩

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Objective 1: Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog. [source: JFM responsibility (M1)]
KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog."
KR2. Evidence at this level's scope bar: "A single team" — ⟨target⟩ by ⟨date⟩

Objective 2: Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team. [source: JFM responsibility (M1)]
KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team."
KR2. Evidence at this level's autonomy bar: "Manages within established goals" — ⟨target⟩ by ⟨date⟩

Objective 3: Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget. [source: JFM responsibility (M1)]
KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget."
KR2. Evidence at this level's complexity bar: "Day-to-day delivery and people issues" — ⟨target⟩ by ⟨date⟩

Objective 4: Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups. [source: JFM responsibility (M1)]
KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups."
KR2. Evidence at this level's impact bar: "Team output and health" — ⟨target⟩ by ⟨date⟩

Objective 5: Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management. [source: JFM responsibility (M1)]
KR1. From ⟨baseline⟩ to ⟨target⟩ by ⟨date⟩ — tied to: "Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management."
KR2. Evidence at this level's decision rights bar: "Owns team execution, hiring input, performance" — ⟨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.

Area	Standard	Target	Due
Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog.	Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."	⟨target⟩	⟨date⟩
Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team.	Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."	⟨target⟩	⟨date⟩
Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget.	Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."	⟨target⟩	⟨date⟩
Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups.	Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."	⟨target⟩	⟨date⟩
Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management.	Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."	⟨target⟩	⟨date⟩

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1. Area: Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog.  [source: JFM responsibility (M1) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."
   Target:   ⟨target⟩   Due: ⟨date⟩

2. Area: Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team.  [source: JFM responsibility (M1) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."
   Target:   ⟨target⟩   Due: ⟨date⟩

3. Area: Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget.  [source: JFM responsibility (M1) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."
   Target:   ⟨target⟩   Due: ⟨date⟩

4. Area: Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups.  [source: JFM responsibility (M1) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."
   Target:   ⟨target⟩   Due: ⟨date⟩

5. Area: Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management.  [source: JFM responsibility (M1) — reused, no distinct responsibility content]
   Standard: Consistent with this level's jfm knowledge-application rubric: "Functional data engineering expert (SQL, Python, ETL, cloud ops) with emerging leadership exposure; applies established practices and runbooks to supervise a unit's daily pipeline work."
   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

"Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog."→ ⟨target⟩ by ⟨date⟩
"Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team."→ ⟨target⟩ by ⟨date⟩
"Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget."→ ⟨target⟩ by ⟨date⟩
"Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups."→ ⟨target⟩ by ⟨date⟩
"Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management."→ ⟨target⟩ by ⟨date⟩

Role calibration

Meets the scope bar: "A single team"→ ⟨target⟩ by ⟨date⟩
Meets the autonomy bar: "Manages within established goals"→ ⟨target⟩ by ⟨date⟩
Meets the complexity bar: "Day-to-day delivery and people issues"→ ⟨target⟩ by ⟨date⟩
Meets the impact bar: "Team output and health"→ ⟨target⟩ by ⟨date⟩
Meets the decision rights bar: "Owns team execution, hiring input, performance"→ ⟨target⟩ by ⟨date⟩
Meets the leadership bar: "Direct people management of one team"→ ⟨target⟩ by ⟨date⟩

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Internal process
  - "Supervises a unit of data engineers building and maintaining ETL pipelines, assigning day-to-day tasks like SQL query development, data cleaning, and basic pipeline maintenance against an established backlog."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M1)]
  - "Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M1)]
  - "Monitors orchestration runs in Airflow/Prefect and pipeline observability dashboards (CloudWatch/Grafana), triaging recurring job failures and resource issues that affect short-term delivery and unit budget."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M1)]
  - "Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M1)]
  - "Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management."  →  ⟨target⟩ by ⟨date⟩   [source: JFM responsibility (M1)]

Role calibration
  - Meets the scope bar: "A single team"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Scope)]
  - Meets the autonomy bar: "Manages within established goals"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Autonomy)]
  - Meets the complexity bar: "Day-to-day delivery and people issues"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Complexity)]
  - Meets the impact bar: "Team output and health"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Impact)]
  - Meets the decision rights bar: "Owns team execution, hiring input, performance"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Decision rights)]
  - Meets the leadership bar: "Direct people management of one team"  →  ⟨target⟩ by ⟨date⟩   [source: level dimension (Leadership)]