Data Engineering — M1

DATADA.DATAENGID906.M1

M1M1 — Manager (Team Lead)high0.90approvedglobalv1

Management of data engineering teams that build and operate data pipelines, warehouses/lakehouses, and ETL/streaming systems. Distinct from Database Administration (operational DBMS uptime/tuning) and Analytics/BI Engineering (semantic layer, dashboards): this focus owns the movement, transformation, modeling, and governance of data at scale across cloud platforms using Spark, Airflow, dbt, Kafka, and Snowflake/Databricks/BigQuery, including ingestion (Fivetran), IaC (Terraform), containerization (Docker/Kubernetes), CI/CD (Jenkins/GitHub), and pipeline observability (Splunk/Grafana/CloudWatch).

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Level

M1 · M1 — Manager (Team Lead) · 3–6 yrs

Function · Focus

Data & Database Engineering · Data Engineering

Market pay (median)

$86k ($67k–$109k)

Pay basis

function-anchored · wide bars

Focus — Data Engineering

Material SKILL differential vs the function baseline.

Responsibilities by level

What this person actually does at each level on the management track — escalating scope, not one generic blob. Your level is highlighted.

M1this profile

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.
Reviews engineers' pipeline code, dbt models, and data quality checks, enforcing established coding standards and file-format conventions (Parquet/Avro) within the team.
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.
Mentors junior engineers on foundational ETL development, SQL/Python, and cloud platform operations (AWS Glue, S3, Athena), pairing during daily standups.
Tracks unit throughput against sprint goals and reports blockers, providing input on staffing and task prioritization to senior management.

Manages a skilled team of data engineers (and occasionally junior leads) delivering robust pipelines and data warehousing solutions on Snowflake/Redshift/BigQuery, owning tactical outcomes against quarterly commitments.
Coordinates cross-functionally with analytics and product teams to integrate new sources via Fivetran/Kafka ingestion and to surface curated data into Looker/BI layers.
Makes judgment calls within known engineering factors on pipeline design trade-offs — Spark vs. dbt transformations, partitioning, and batch vs. streaming — for the team's assigned workloads.
Owns the team's data quality and SLA targets, defining monitoring and alerting expectations (Splunk/Grafana) and driving remediation of recurring incidents.
Develops individual engineers through written development plans, calibrating performance and supporting promotion of independent contributors to senior IC.

Leads the data engineering department, owning operations and an annual budget for pipelines, warehouse infrastructure, orchestration, and the CI/CD toolchain (Jenkins/GitHub).
Evaluates diverse engineering issues and cost/performance trends across multiple cloud services (Snowflake/Databricks/BigQuery), directing tuning of Spark jobs, Delta Lake tables, and NoSQL/Postgres operational stores.
May lead other managers or cross-functional professionals, coordinating with security, infra, and analytics teams on shared data initiatives and on Terraform-managed environments.
Establishes and enforces team-level data governance, data modeling conventions, and security standards for the department's deliverables, and owns hiring and development plans for the team.
Owns capacity planning and vendor/tool selection (Fivetran, dbt, Airflow, Databricks) to meet departmental objectives within budget.

Manages multiple data engineering teams or a critical platform function, setting the multi-team architecture roadmap for batch and real-time streaming (Kafka/Flink/Kinesis) across the function.
Sets strategic policies for data governance, security, and multi-cloud system design, making build-vs-buy and platform-consolidation calls where failure could jeopardize critical business data flows.
Engages senior leaders on data strategy, translating analytics, ML, and product needs into a prioritized, resourced multi-team engineering plan and budget.
Defines cross-team standards for IaC (Terraform), containerization (Docker/Kubernetes), and CI/CD so that pipelines deploy reliably and reproducibly at scale.
Builds the leadership bench by developing managers and senior engineers, defining org structure, and creating upskilling and development pathways for complex initiatives.

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.
Defines enterprise data architecture and lakehouse/warehouse standards (Databricks/Snowflake, Delta Lake) that govern how analytics, ML, and reporting consume data company-wide.
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.
Resolves complex, org-wide data problems — multi-cloud consolidation, cost governance, and platform reliability — by defining the methods and reference architectures all teams adopt.
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.

Level guidelines

The universal leveling rubric applied to this function — how scope, complexity, collaboration, and experience step up across levels.

Level	Knowledge & Application	Complexity & Problem Solving	Collaboration & Interaction	Typical Degree & Years
M1	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.	Limited scope; resolves operational pipeline and data quality issues using established practices, within short-term unit goals and budget.	Daily interactions with engineering staff and immediate peers; coordinates task execution and escalates blockers.	Seasoned data engineer who has moved into first-line supervision; deep hands-on expertise with some leadership exposure.
M2	Applies deep data engineering judgment to lead a skilled team, making pipeline design and tooling decisions within known engineering factors.	Exercises judgment within known factors on pipeline design, partitioning, and batch/stream trade-offs for assigned workloads; owns tactical SLA outcomes.	Cross-functional cooperation with analytics, product, and infra teams to integrate sources and meet SLAs.	Established supervisor/specialist with several years of team leadership beyond the M1 supervisory bar.
M3	Manages a department's data engineering operations and budget; evaluates diverse issues and cost/performance trends to set team conventions, governance, and tooling.	Addresses diverse engineering issues and evaluates data trends to improve pipeline performance, cost, and reliability across multiple cloud services.	Leads functional or cross-functional data teams; partners with security, infra, and analytics leadership on shared initiatives.	Experienced manager of data engineering professionals with multi-year budget and operations ownership.
M4	Sets strategic data architecture, streaming, and governance policies across multiple teams, aligned to business objectives and resourcing.	Solves complex multi-team architecture, multi-cloud, and governance problems where failures could jeopardize critical business data flows.	Engages senior leaders on functional data strategy; orchestrates across multiple teams and stakeholder organizations.	Senior leader with extensive, complex team/org leadership in data engineering across multiple teams or a critical function.
M5	Directs division-wide data platform strategy through managers; defines enterprise methods, reference architectures, and standards.	Resolves complex org-wide data challenges (multi-cloud consolidation, cost governance, reliability) and defines the methods adopted across all teams.	Influences executives and major stakeholders on key data platform decisions with business-wide impact.	Director-level leader with second-level management experience and a track record of data platform strategy.

Skills

Focus-specific skills the role applies — the relevance layer beyond the occupational base.

Data architecture: Designing strategies for enterprise databases, data warehouse/lakehouse systems, and platform-wide standards across multiple teams and clouds.
Data governance: Establishing standards, frameworks, and policies for data operations, security, and management across teams and the organization.
Data warehousing: Directing the design and implementation of warehouse/lakehouse solutions (Snowflake, Databricks, BigQuery, Redshift, Delta Lake) for analytical data storage.
ETL development: Overseeing the build of extract-transform-load processes that ensure data quality and move data through pipelines at scale.
Workflow orchestration: Setting standards for scheduling, monitoring, and managing pipeline workflows using Airflow, Prefect, or Dagster.
Real-time streaming: Directing event-streaming pipelines using Kafka, Kinesis, or Flink for low-latency and streaming workloads.
Distributed processing: Guiding the processing of terabytes of data across clusters, typically using Spark/PySpark, for batch and streaming workloads.
Data transformation: Establishing in-warehouse transformation conventions with dbt as the standard tooling.
Infrastructure as code & CI/CD: Standardizing reproducible deployments using Terraform, Docker/Kubernetes, and CI/CD pipelines (Jenkins, GitHub).
Pipeline observability: Setting expectations for monitoring, alerting, and logging of pipelines using Splunk, Grafana, and CloudWatch.
Budget & operations management: Owning the operating budget, capacity planning, and vendor/tool selection for a data engineering team or department.
Mentoring & people development: Teaching and upskilling engineers, creating individual development plans, and building the leadership bench and succession for the org.

Provenance

The evidence base behind this profile — every layer is sourced; quality is scored by an adversarial review panel (1–5; passes at ≥4 on the minimum dimension).

Level differentiation4.5Focus specificity5.0Concreteness5.0Factual accuracy4.5Real-world coverage4.5

12 sources

Level — M1 — Manager (Team Lead)

Front-line people manager of a single team; owns delivery, coaching, and execution.

Scope: A single team
Autonomy: Manages within established goals
Complexity: Day-to-day delivery and people issues
Impact: Team output and health
Decision rights: Owns team execution, hiring input, performance
Leadership: Direct people management of one team
Typical experience: 3–6 yrs

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