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Key Responsibilities and Required Skills for Data Planner

💰 $85,000 - $160,000

Data StrategyData EngineeringAnalyticsProduct

🎯 Role Definition

The Data Planner defines and coordinates the roadmap for data platforms, pipelines, and data product delivery. This role combines technical fluency in cloud data platforms and ETL/orchestration tooling with business acumen to translate strategic priorities into prioritized engineering work, SLAs, and budget forecasts. The Data Planner drives data governance adoption, capacity planning, cost optimization, and clear data contracts between producers and consumers to ensure trustworthy, discoverable, and performant data at scale.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst or Senior Data Analyst
  • Data Engineer or ETL Developer
  • Business Analyst with analytics delivery experience

Advancement To:

  • Senior Data Planner / Lead Data Planner
  • Data Strategy Manager or Data Operations Manager
  • Data Architect or Head of Data Planning / Director of Data Platforms

Lateral Moves:

  • Data Product Manager
  • Data Governance Lead
  • Analytics Manager

Core Responsibilities

Primary Functions

  • Develop and maintain a rolling 6–18 month data roadmap that aligns data platform investment, pipeline development, and analytics releases with business strategic priorities and product roadmaps.
  • Work with executive stakeholders and product owners to translate business goals and KPIs into prioritized data workstreams, user stories, and acceptance criteria for engineering teams.
  • Create and enforce data contracts and SLAs between data producers and consumers, including schema expectations, availability windows, latency objectives, and alerting requirements.
  • Lead capacity planning and forecasting for cloud data platforms (compute, storage, streaming), modeling cost impact of feature launches, ingestion growth, and retentions to produce monthly and quarterly budgets.
  • Partner with data engineering to design scalable ETL/ELT architectures, selecting appropriate orchestration (Airflow, Prefect), transformation (dbt), and processing (Spark, Beam) patterns for each use case.
  • Own the data catalog and metadata strategy (Alation, Collibra, or open-source solutions), driving metadata capture, lineage, and discoverability to reduce time-to-insight.
  • Implement data governance practices including data classification, access controls, PII masking, and retention policies to support compliance (GDPR, CCPA) and security requirements.
  • Define and operationalize data quality frameworks and monitoring (data observability), establishing metrics, thresholds, and remediation flows for inaccurate or missing data.
  • Develop measurement frameworks and instrumentation requirements so product and analytics teams can reliably track feature performance and business KPIs.
  • Coordinate cross-functional release planning for data platform changes, migrations, and major pipeline launches to minimize downtime and consumer impact.
  • Maintain a prioritized backlog of technical debt, schema evolution requests, and performance tuning tasks; work with engineering to schedule and validate fixes.
  • Create runbooks, incident response procedures, and post-incident reviews for data outages and quality incidents; track MTTR and remediation improvements.
  • Lead vendor evaluations and manage relationships with data platform vendors, managed services, and third-party data providers including contract, SLA, and cost optimization discussions.
  • Drive standardization of data modeling practices (star schemas, normalized models, data vault) and promote reuse of core business entities across analytics and ML teams.
  • Collaborate with ML engineering and data science teams to plan and provision data pipelines, feature stores, and training datasets while enforcing reproducibility and lineage.
  • Produce executive-level reporting and dashboards on data platform health, pipeline performance, cost trends, and roadmap progress to inform leadership decisions.
  • Facilitate stakeholder working sessions to elicit requirements, negotiate priorities, and align expectations across product, analytics, legal, and security teams.
  • Design and implement ingestion strategies for streaming and batch data, including schema validation, backfill approaches, and disaster recovery plans.
  • Establish metrics and guardrails for data access requests, ensuring least-privilege principles and automating provisioning workflows where possible.
  • Drive cost governance initiatives such as query optimization, partitioning, clustering, and retention policy enforcement to lower per-query and storage costs.
  • Define and track KPIs for data enablement such as time-to-data, data discovery adoption, SLA compliance, and percent of production-ready datasets.
  • Support schema evolution planning and versioning practices to avoid breaking downstream consumers and enable graceful migrations.
  • Mentor junior planners and data operations staff on planning best practices, capacity modeling, and stakeholder communication.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Assist with onboarding and enablement materials for new data consumers and internal teams.
  • Evaluate emerging data tools and patterns and pilot innovations that can improve developer productivity or reduce costs.

Required Skills & Competencies

Hard Skills (Technical)

  • Expert SQL skills for data exploration, validation, and query cost optimization across cloud warehouses (Snowflake, BigQuery, Redshift).
  • Practical experience with at least one cloud data platform (AWS, GCP, Azure) and related services (S3/GCS, IAM, Glue).
  • Hands-on familiarity with ETL/ELT and orchestration tools such as Apache Airflow, dbt, Prefect, NiFi, or equivalent.
  • Experience with data transformation frameworks and modeling patterns (dbt, star schema, data vault).
  • Knowledge of streaming platforms and ingestion technologies (Kafka, Kinesis, Pub/Sub) and real-time pipeline considerations.
  • Familiarity with data cataloging and governance tools (Collibra, Alation, Amundsen) and metadata APIs.
  • Experience implementing data quality and observability tooling (Great Expectations, Monte Carlo, Soda).
  • Understanding of API design, RESTful interfaces, and data ingestion via APIs and change-data-capture (CDC) tools (Debezium).
  • Hands-on experience with analytics and BI tools (Looker, Tableau, Power BI) to validate datasets and business metrics.
  • Proficiency in one scripting language (Python, SQL-based stored procedures) for automation, cost modeling, and lightweight ETL tasks.
  • Familiarity with containerization and CI/CD tooling (Git, GitHub Actions, Jenkins, Docker) for reproducible data deployments.
  • Knowledge of security, privacy, and compliance standards (PII handling, GDPR, CCPA) and the ability to enforce policies programmatically.

Soft Skills

  • Strong stakeholder management and cross-functional communication skills; able to synthesize technical tradeoffs for business audiences.
  • Prioritization and road-mapping ability: balancing short-term delivery with long-term platform investments.
  • Analytical problem-solving with a data-driven mindset; comfortable building models to forecast capacity, cost, and resource needs.
  • Project management skills and experience working in agile teams; able to drive initiatives to completion.
  • Detail-oriented with strong documentation habits (runbooks, data contracts, lineage documentation).
  • Influencing and negotiation skills to align competing stakeholder priorities and secure necessary resources.
  • Empathy and customer-centric thinking for internal data consumers and analytics teams.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, Statistics, Business Analytics, or a related field.

Preferred Education:

  • Master’s degree in Data Science, Business Analytics, MBA, or a related advanced degree.
  • Relevant professional certifications (SnowPro, Google Cloud Professional Data Engineer, AWS Certified Data Analytics, dbt Labs Certification) are a plus.

Relevant Fields of Study:

  • Computer Science
  • Data Science / Analytics
  • Information Systems
  • Statistics / Applied Mathematics
  • Business / Operations Research

Experience Requirements

Typical Experience Range:

  • 3–7+ years working in data roles such as data engineering, analytics engineering, data operations, or product analytics with increasing ownership of planning, roadmapping, or governance.

Preferred:

  • 5+ years experience in data platform planning, capacity forecasting, or data governance in cloud-first environments.
  • Demonstrated track record of owning cross-functional delivery, cost optimization for data platforms, and implementing data quality/governance practices at scale.
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