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Key Responsibilities and Required Skills for Data Technician

💰 $45,000 - $85,000

DataAnalyticsInformation TechnologyOperations

🎯 Role Definition

The Data Technician is an operational data professional responsible for ingesting, validating, transforming, and delivering high-quality data to analytics, BI, and engineering teams. The role focuses on hands-on data processing, recurring ETL/job orchestration, data quality monitoring, and operational support for data pipelines and reporting systems. The ideal candidate combines strong technical skills (SQL, scripting, ETL tools, basic cloud familiarity) with attention to data governance, documentation, and stakeholder communication.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst (entry-level with heavy ETL or reporting focus)
  • IT Support Technician or Operations Technician with SQL/scripting exposure
  • Junior Database Administrator or BI Support Specialist

Advancement To:

  • Data Engineer
  • Senior Data Technician / Data Operations Lead
  • ETL Developer or Pipeline Engineer

Lateral Moves:

  • Business Intelligence Analyst
  • Data Quality Analyst
  • Reporting/Analytics Engineer

Core Responsibilities

Primary Functions

  1. Develop, run, and maintain ETL/ELT jobs and data ingestion pipelines using SQL, Python, Bash, or ETL tools (e.g., Informatica, Talend, Fivetran/Airbyte), ensuring timely and repeatable delivery of datasets to downstream consumers.
  2. Monitor production data pipelines and scheduled jobs using orchestration platforms (Airflow, Prefect, Azure Data Factory, or equivalent); respond to alerts, troubleshoot failures, and implement fixes or escalation procedures.
  3. Perform daily and periodic data validation, reconciliation, and anomaly detection to confirm completeness, accuracy, and integrity of ingested data across systems (APIs, flat files, event streams, and databases).
  4. Build and maintain automated data quality checks, unit tests, and reconciliation scripts; log and track data quality incidents, drive root-cause analysis, and implement preventative measures.
  5. Transform raw source data into curated, analytics-ready tables using SQL and scripting, applying standard data modeling conventions, partitioning strategies, and metadata tagging for discoverability.
  6. Extract, normalize, and parse data from varied sources (CSV, JSON, XML, APIs, log files) and convert to standardized formats required by the data warehouse, lakehouse, or BI tools.
  7. Load and maintain datasets in relational databases and cloud data warehouses (Postgres, MySQL, Redshift, BigQuery, Snowflake, Azure Synapse) with attention to performance, indexing, and storage optimization.
  8. Support data onboarding for new sources by creating ingestion specs, field mappings, sample loads, and validation plans; coordinate with source owners and platform engineers to enable reliable feeds.
  9. Troubleshoot root causes of data drift, missing partitions, schema changes, and upstream regressions; implement schema detection or change management processes to minimize downstream breaks.
  10. Collaborate with data stewards, business analysts, and product owners to document data definitions, lineage, SLAs, and ownership for critical datasets and reports.
  11. Maintain clear operational runbooks, playbooks, and run charts for recurring processes, incident response, and backfill procedures to enable quick recovery and knowledge transfer.
  12. Perform ad-hoc extract and transform tasks to support urgent analytics, financial reporting, regulatory requests, or operational investigations with fast turnaround and traceability.
  13. Implement incremental load strategies, deduplication logic, and CDC (change data capture) patterns to support efficient data updates and reduce reprocessing overhead.
  14. Provide first-level support to BI/reporting users by troubleshooting dashboard anomalies, data mismatches, and permission or refresh issues; escalate complex bugs to engineering as needed.
  15. Maintain and update data catalogs and metadata repositories (Collibra, Alation, DataHub, or internal catalogs) to improve data discoverability and governance compliance.
  16. Optimize SQL queries and transformation logic for scalability and reduced compute cost; profile query performance and propose indexing, partitioning, or refactoring changes.
  17. Enforce data security and privacy practices by applying masking, encryption, least-privilege access, and ensuring compliance with PII/PHI handling policies across datasets.
  18. Automate repetitive operational tasks (scheduling, monitoring, cleanup) using scripts, cron jobs, or automation frameworks to improve reliability and reduce manual toil.
  19. Assist with data migration, archival, and retention strategies to meet regulatory retention policies and manage storage costs effectively.
  20. Participate in sprint planning, stand-ups, and cross-functional reviews to align operational priorities with analytics, product, and engineering roadmaps.
  21. Create clear documentation, sample queries, and FAQs for end users and analysts to reduce support requests and accelerate self-service analytics adoption.
  22. Evaluate and recommend tooling or process improvements (observability, logging, deployment patterns) to improve pipeline resilience, traceability, and developer experience.

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: writing complex joins, window functions, CTEs, and performance tuning for OLAP workloads.
  • Scripting: Python (pandas, sqlalchemy), Bash, or R for transformations, automation, and quick ETL tasks.
  • ETL/ELT Tools & Orchestration: experience with Apache Airflow, Prefect, Azure Data Factory, or commercial ETL platforms.
  • Cloud Data Platforms: hands-on with Snowflake, BigQuery, Redshift, Azure Synapse, or equivalent.
  • Data Formats & APIs: working knowledge of CSV, Parquet, JSON, Avro, XML, and RESTful/streaming API integrations.
  • Data Quality & Testing: unit tests for data, validation frameworks, reconciliation, and anomaly detection approaches.
  • BI & Reporting Tools: exposure to Tableau, Power BI, Looker, or similar for supporting dashboards and troubleshooting data issues.
  • Data Modeling & Warehousing: understanding star/snowflake schemas, dimensional modeling, incremental loads, and CDC.
  • Monitoring & Logging: familiarity with observability tools, alerts, and logging frameworks (Prometheus, Grafana, CloudWatch, ELK).
  • Version Control & DevOps Practices: Git, CI/CD basics for pipeline deployment and change control.
  • Basic Linux/Windows server operations, cron scheduling, and job management.
  • Knowledge of data privacy, governance, and security best practices (masking, role-based access control, GDPR/CCPA awareness).

(At least 10 of these are commonly required in Data Technician job postings.)

Soft Skills

  • Strong analytical reasoning and attention to detail for detecting data anomalies and ensuring accuracy.
  • Clear written and verbal communication for documenting processes and interacting with stakeholders.
  • Problem-solving mindset with persistence in troubleshooting production incidents and implementing durable fixes.
  • Time management and prioritization to balance operational firefighting with recurring improvements.
  • Team collaboration and customer-service orientation when supporting analysts and business users.
  • Adaptability to work with varied systems, changing schemas, and evolving business requirements.
  • Proactive continuous improvement focus — proposing automations and efficiency gains.
  • Teaching and knowledge-sharing to enable self-service analytics across the organization.

Education & Experience

Educational Background

Minimum Education:

  • Associate degree in Information Technology, Computer Science, Data Analytics, or equivalent practical experience. High-performing candidates with relevant certifications and hands-on experience will be considered.

Preferred Education:

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

Relevant Fields of Study:

  • Computer Science
  • Data Science / Analytics
  • Information Systems
  • Statistics / Applied Mathematics
  • Software Engineering
  • Information Technology

Experience Requirements

Typical Experience Range: 1–5 years working in data operations, ETL support, database administration, or analytics engineering.

Preferred:

  • 2–4 years of hands-on experience building and maintaining ETL pipelines, working with cloud data warehouses, and supporting production data environments.
  • Demonstrable experience with SQL-first workflows, data quality frameworks, and troubleshooting operational incidents in production data systems.
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