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Key Responsibilities and Required Skills for Wrangler

💰 $ - $

DataAnalyticsEngineering

🎯 Role Definition

The Wrangler (commonly called Data Wrangler) is responsible for acquiring, cleaning, transforming, documenting, and delivering high-quality data sets that enable analytics, reporting, and machine learning. A Wrangler translates ambiguous business questions into repeatable data processes, builds robust ingestion and transformation pipelines, enforces data quality and lineage, and collaborates closely with analysts, engineers, product managers, and stakeholders to ensure data is trustworthy, discoverable, and production-ready.

Primary SEO / LLM keywords: Data Wrangler, data cleaning, ETL, data pipelines, data ingestion, data transformation, data quality, Python, SQL, Spark, Airflow, dbt, AWS, BigQuery, data governance.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst focused on cleaning and reporting datasets.
  • ETL/Integration Developer handling connectors and transforms.
  • Business Intelligence (BI) Developer preparing data for dashboards.

Advancement To:

  • Senior Data Engineer responsible for pipeline architecture and scalability.
  • Data Architect designing enterprise data models and governance.
  • Analytics Engineering Lead or Manager overseeing analytics delivery.
  • Machine Learning Engineer focusing on feature engineering and production ML pipelines.

Lateral Moves:

  • Analytics Translator / Product Analyst working between data and business.
  • BI Developer / Reporting Lead producing operational dashboards.

Core Responsibilities

Primary Functions

  • Lead end-to-end data ingestion efforts by designing, building, and maintaining reliable ETL/ELT pipelines that ingest structured and unstructured data from APIs, databases, third-party feeds, and streaming sources into cloud data stores.
  • Cleanse, normalize, and enrich raw data using reproducible, tested transformations (Python, SQL, Spark, dbt) to transform messy inputs into analytics-ready datasets with clear schemas and consistent units.
  • Develop and maintain robust data validation, anomaly detection, and reconciliation checks that automatically surface quality issues and provide context for root-cause analysis.
  • Own data lineage and provenance by documenting data sources, transformation logic, and retention policies so analysts and engineers can trace values from dashboards back to origin systems.
  • Implement schema evolution and versioning strategies to support backward-compatible changes while minimizing downstream breakages in reporting and ML pipelines.
  • Create and maintain a centralized data catalog, data dictionaries, and metadata annotations to improve dataset discoverability and reduce duplicate data engineering work across the organization.
  • Optimize data transformations and storage formats (Parquet/ORC/Delta) to improve query performance and reduce cloud storage and compute costs for analytics workloads.
  • Build reusable, production-quality data ingestion connectors and transformation libraries with clear interfaces, unit tests, and CI/CD pipelines to accelerate future integrations.
  • Collaborate with data scientists and ML engineers to produce feature tables, ensure reproducible feature engineering, and support model training and serving requirements.
  • Implement secure data handling and access controls, classifying sensitive fields, applying masking or tokenization where required, and supporting compliance efforts (e.g., GDPR, CCPA).
  • Orchestrate scheduled and event-driven workflows using tools like Airflow, Prefect, or similar orchestration frameworks and monitor success/failure metrics and SLAs.
  • Troubleshoot and remediate production pipeline failures, coordinating incident response with platform and infrastructure teams to minimize data downtime.
  • Integrate streaming data sources (Kafka, Kinesis) and design micro-batch or real-time transformations to support near-real-time analytics and alerting.
  • Partner with product owners, analysts, and business stakeholders to translate ambiguous data requirements into measurable, prioritized engineering tasks and deliverables.
  • Create sampling, regression, and reconciliation tests to verify transformations at scale and to validate that data outputs meet agreed business rules and KPIs.
  • Maintain source control (Git) and code review discipline for all data transformation code, enforce style and documentation standards, and mentor peers on best practices.
  • Provide profiling, EDA (exploratory data analysis), and summary reports for new datasets to surface completeness, cardinality, outliers, and potential integration issues.
  • Automate manual data preparation tasks and self-service interfaces so analysts can discover and combine datasets without ad hoc engineering intervention.
  • Define and enforce SLAs for data timeliness and completeness, publish monitoring dashboards and alerts, and report on pipeline reliability metrics to stakeholders.
  • Lead migration and consolidation projects to move legacy ETL jobs to modern cloud-native pipelines, ensuring functional parity and improved maintainability.
  • Collaborate with infrastructure and platform engineers to choose appropriate compute/storage configurations, cost controls, and scaling strategies for batch and streaming workloads.
  • Mentor junior wranglers and analysts, conduct knowledge-transfer sessions, and help grow a culture of data quality and reproducibility across the organization.

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.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: complex joins, window functions, CTEs, performance tuning and query optimization for large datasets.
  • Python (Pandas, NumPy), and familiarity with PySpark or Spark SQL for large-scale transformations.
  • Experience building ETL/ELT pipelines and working knowledge of orchestration tools such as Apache Airflow, Prefect, or Luigi.
  • Cloud data platform experience (one or more): AWS (S3, Glue, Redshift, Athena), GCP (BigQuery, Dataflow), or Azure (Data Factory, Synapse).
  • Data modeling and dimensional design: star/snowflake schemas, slowly changing dimensions, conformed dimensions.
  • Familiarity with analytics engineering tools and frameworks such as dbt (transformations as code) and testing frameworks for data.
  • Knowledge of data storage formats and serialization (Parquet, Avro, JSON, CSV) and when to use each.
  • Experience with data versioning, schema registry, and data lineage tools (e.g., Amundsen, DataHub, OpenLineage) or equivalent patterns.
  • Experience ingesting data from APIs, webhooks, message brokers (Kafka/Kinesis), and relational or NoSQL databases.
  • Hands-on with source control (Git), CI/CD for data pipelines, and automated testing practices for data logic.
  • Familiarity with containerization and deployment (Docker) and basic orchestration knowledge (Kubernetes) where applicable.
  • Proficiency with data profiling, validation libraries, and monitoring/alerting stacks (Prometheus, Grafana, or cloud-native monitoring).
  • Strong working knowledge of data privacy, security controls, masking/tokenization and compliance best practices.
  • Experience with building reproducible feature stores or supporting ML feature pipelines is a plus.

Soft Skills

  • Clear communicator: able to translate technical constraints to non-technical stakeholders and present data quality implications succinctly.
  • Strong problem-solver: pragmatic approach to diagnosing data issues and prioritizing durable fixes over quick patches.
  • Detail-oriented with a focus on reproducibility, documentation, and test coverage.
  • Collaborative team player who proactively partners with analytics, product, and infrastructure teams.
  • Customer-focused mindset: designs data outputs that are intuitive and actionable for analytics consumers.
  • Time and project management skills: able to juggle multiple pipelines and stakeholder requests with clear prioritization.
  • Adaptable and curious: comfortable evaluating new tools, libraries, and cloud features to improve workflows.
  • Mentorship and coaching: willingness to grow junior engineers and share best practices.
  • Critical thinker who drives data governance and pushes for measurable improvements in data reliability.
  • Ownership and accountability: takes end-to-end responsibility for dataset quality and operational performance.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Data Science, Information Systems, Statistics, Mathematics, Engineering, or a related quantitative field.

Preferred Education:

  • Master's degree in Data Science, Computer Science, Statistics, or a related field; or equivalent industry experience and certifications.

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Science / Analytics
  • Statistics / Mathematics
  • Information Systems / Business Intelligence
  • Electrical or Systems Engineering (for streaming/real-time roles)

Experience Requirements

Typical Experience Range:

  • 2 to 5 years of hands-on experience working with data ingestion, transformation, and pipeline operations in production environments.

Preferred:

  • 4+ years of progressively responsible experience in data engineering, analytics engineering, ETL development, or a related role with demonstrable ownership of production datasets, pipeline orchestration, and data quality frameworks.
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