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Key Responsibilities and Required Skills for Data Specialist

💰 $60,000 - $100,000

DataAnalyticsEngineeringBusiness Intelligence

🎯 Role Definition

A Data Specialist is a domain expert responsible for collecting, cleaning, integrating, and delivering high-quality data to support analytics, reporting, and decision-making across the organization. This role acts as the bridge between business stakeholders, data engineers, analysts, and data governance teams to ensure reliable, timely, and well-documented data products. The Data Specialist designs and maintains ETL/data pipeline processes, enforces data quality best practices, operationalizes datasets in cloud and on-prem platforms, and contributes to a data-literate culture by providing documentation and training.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst transitioning from operational reporting and Excel-driven workflows.
  • ETL/BI Developer with experience building pipelines and dashboards.
  • Database Administrator or Systems Analyst with data integration responsibilities.

Advancement To:

  • Senior Data Specialist / Lead Data Specialist
  • Data Engineer or Senior Data Engineer
  • Analytics Manager, Data Product Manager, or Data Governance Lead

Lateral Moves:

  • Business Intelligence Developer
  • Data Quality Analyst
  • Machine Learning Operations (MLOps) Engineer

Core Responsibilities

Primary Functions

  • Design, implement, and maintain reliable ETL and ELT pipelines that ingest, transform, and load structured and semi-structured data from multiple sources (APIs, databases, cloud storage, flat files) into the analytics environment using tools such as SQL, Python, dbt, Apache Airflow, or native cloud services.
  • Perform deep data profiling and source-to-target analysis to identify inconsistencies, missing values, duplicates, and schema drift, and implement automated remediation or alerting where appropriate to maintain data integrity.
  • Write modular, production-quality SQL for complex joins, window functions, aggregations, and performance-optimized queries to support ad-hoc analysis and upstream data models.
  • Own the data cataloging and metadata processes: document source lineage, field definitions, update cadence, ownership, and transformation logic to ensure discoverability and trust for business users and data teams.
  • Implement and monitor data quality rules and validation tests (e.g., referential integrity, range checks, null constraints) using unit-test frameworks or CI pipelines, and work with owners to resolve issues.
  • Build and maintain ingestion connectors for third-party APIs and streaming sources, applying batching, rate-limiting, checkpointing, and backfill strategies to ensure robust data delivery.
  • Collaborate with product managers, analysts, and stakeholders to translate business requirements into technical specifications, data models, and measurable KPIs that align with company objectives.
  • Design canonical data models and dimensional schemas (star/snowflake) for reporting and analytics use cases; optimize for query performance and business usability.
  • Instrument and tune data warehouse performance on cloud platforms (Snowflake, BigQuery, Redshift) including clustering, partitioning, materialized views, and cost-conscious compute scheduling.
  • Implement data access controls and row-level security strategies in analytical databases and BI tools to ensure compliance with privacy and internal policies.
  • Support data ingestion and transformation workflows for machine learning features and model inputs, ensuring reproducibility and traceability of feature engineering steps.
  • Automate routine data operations, monitoring, and alerting using scripting and orchestration tools to reduce manual intervention and mean time to repair.
  • Prepare and deliver regular operational and strategic reports (dashboards, scheduled extracts) for finance, marketing, sales, and operations teams, ensuring timeliness and accuracy.
  • Execute data migration, consolidation, and master data management activities during system upgrades, mergers, or data platform modernization projects.
  • Troubleshoot production pipelines, debug failures, perform root-cause analysis, and implement permanent fixes and retrospective documentation.
  • Enforce best practices for version control, branching, and code review for SQL, Python, and ETL codebases using Git and CI/CD workflows.
  • Ensure compliance with data privacy regulations (GDPR, CCPA) by implementing anonymization, masking, and consent-driven data flows in collaboration with legal and security teams.
  • Develop and maintain technical documentation, runbooks, and onboarding guides for data sources, pipelines, and dashboards to accelerate team productivity and knowledge transfer.
  • Provide regular training, office hours, and consultation for business users on data definitions, available datasets, and analytics tooling to improve self-service adoption.
  • Evaluate and prototype new data tools and platforms (data lakes, lakehouses, streaming frameworks) and present cost/benefit analyses to leadership for strategic adoption.
  • Participate in cross-functional agile ceremonies and sprint planning; estimate tasks, prioritize backlog items, and deliver data features in iterative increments.
  • Establish and monitor data SLAs and KPIs for dataset freshness, completeness, and accuracy; report metrics to stakeholders and take corrective action as needed.

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, query optimization) and experience tuning queries for data warehouses.
  • Proficiency in Python for data ingestion, transformation, scripting, and automation (pandas, requests, sqlalchemy).
  • Experience building and maintaining ETL/ELT pipelines with tools such as dbt, Apache Airflow, Talend, Informatica, or native cloud orchestrators.
  • Familiarity with cloud data platforms and managed warehouses: Snowflake, Google BigQuery, Amazon Redshift, or Azure Synapse.
  • Knowledge of data modeling techniques (dimensional modeling, normalization, master data models) and schema design.
  • Experience with BI and dashboarding tools like Tableau, Power BI, Looker, or Qlik for delivering reports and self-service analytics.
  • Hands-on experience with data quality frameworks, testing tools, and implementation of validation rules.
  • Understanding of data governance, metadata management, data lineage, and catalog tools (Collibra, Alation, Amundsen).
  • Experience with streaming and messaging technologies (Kafka, Kinesis) or batch ingestion strategies for high-volume sources.
  • Familiarity with APIs, RESTful services, authentication patterns, and building connectors to external systems.
  • Experience working with version control (Git), CI/CD pipelines for data projects, and code review best practices.
  • Basic knowledge of statistics and data analysis methods to support reporting accuracy and contextual interpretation.
  • Exposure to containerization and deployment tools (Docker, Kubernetes) for running reproducible data workloads is a plus.
  • Working knowledge of data privacy and security controls, including data masking, anonymization, and role-based access control.
  • Ability to write clear technical documentation and maintain data dictionaries and runbooks.

Soft Skills

  • Strong communication skills: able to explain technical details to non-technical stakeholders and align expectations.
  • Analytical thinking with a rigorous, detail-oriented approach to diagnosing data issues and designing robust solutions.
  • Stakeholder management: able to prioritize competing requests, negotiate scope, and set realistic delivery timelines.
  • Problem-solving mindset with a focus on automation and operational excellence.
  • Collaboration and teamwork: experience working closely with analytics, engineering, product, and business teams.
  • Adaptability and continuous learning orientation to keep pace with evolving data tools and practices.
  • Time management and organization skills to manage multiple data products and support SLAs.
  • Customer-focused approach: balances technical correctness with business usability and urgency.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, or a related technical field OR equivalent practical experience with demonstrable data project outcomes.

Preferred Education:

  • Master's degree in Data Science, Analytics, Computer Science, Business Analytics, or similar advanced technical/business degree.
  • Relevant certifications such as Google Cloud Professional Data Engineer, SnowPro, AWS Certified Data Analytics, or dbt Fundamentals are a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 2–6 years working in data engineering, analytics engineering, ETL, or related data-focused roles.

Preferred:

  • 3+ years in a role building or maintaining production data pipelines and analytics workflows.
  • Demonstrated experience in cloud data platforms (Snowflake, BigQuery, Redshift) and orchestration tools (Airflow, dbt).
  • Proven track record supporting business stakeholders with timely, accurate data products and documentation.
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