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Key Responsibilities and Required Skills for Data Technology Analyst

💰 $ - $

DataTechnologyAnalyticsEngineering

🎯 Role Definition

The Data Technology Analyst is a hybrid technical-analytical role responsible for designing, building, operating, and improving data platforms, pipelines, and analytics products that enable business decision-making. This role sits at the intersection of data engineering, analytics, and business translation: you will develop ETL/ELT pipelines, enforce data quality and governance, partner with product and business teams to translate requirements into scalable data solutions, and support downstream BI, ML and reporting consumers. Success requires strong SQL and scripting skills, familiarity with cloud data ecosystems, and the ability to communicate technical trade-offs to non-technical stakeholders.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst or Business Intelligence (BI) Analyst transitioning into platform work
  • Junior Data Engineer or ETL Developer with hands-on pipeline experience
  • Systems Analyst / Database Developer with SQL and ETL background

Advancement To:

  • Senior Data Technology Analyst / Lead Data Engineer
  • Data Platform Engineer or Data Architect
  • Analytics Engineering Lead / Manager, Data Engineering or Analytics

Lateral Moves:

  • BI Developer / Dashboard Engineering
  • Data Governance or Data Quality Specialist
  • Machine Learning Engineer (with additional ML skills)

Core Responsibilities

Primary Functions

  • Design, build and maintain robust, production-grade data pipelines using ETL/ELT frameworks (Airflow, dbt, Informatica, Talend, custom Python/SQL jobs) to reliably ingest, transform, and deliver data to analytics and downstream applications.
  • Author and optimize complex SQL queries and analytical models to support reporting, KPIs and ad-hoc business requests, ensuring performance and cost-effective execution on cloud warehouses (Snowflake, BigQuery, Redshift).
  • Implement and maintain data modeling and schema design (dimensional modeling, star/snowflake schemas, normalized models) to support BI tooling, analytics engineering, and downstream consumers.
  • Collaborate with product managers, business stakeholders, and data scientists to translate business requirements into technical specifications, prioritizing features and delivering measurable outcomes.
  • Develop, test, and deploy data transformation logic following software engineering best practices: version control (Git), CI/CD pipelines, automated testing, and code review workflows.
  • Monitor data pipelines and platform health with observability tools, set up alerting and incident response for pipeline failures, data drift, and SLA breaches, and perform root cause analysis to prevent recurrence.
  • Implement and enforce data quality controls and validation checks (unit tests, anomaly detection, reconciliations) across ingestion and transformation stages to ensure accuracy and trustable analytics.
  • Manage and optimize data storage, partitioning, and query performance on cloud data platforms, balancing compute, storage, and cost considerations while ensuring fast analytics access.
  • Build and maintain reusable analytics engineering artifacts (dbt models, macros, schemas, documented transformation logic) to accelerate delivery and reduce duplication across teams.
  • Design and document data lineage, metadata, and catalog integration (e.g., using tools like Data Catalog, Alation, Collibra, or open-source metadata solutions) to improve discoverability and governance.
  • Integrate and maintain structured and semi-structured data sources (APIs, event streams, S3/GCS/Azure Blob, on-prem databases), normalizing formats (JSON, Parquet, Avro) for consistent downstream use.
  • Implement role-based access controls, encryption standards, and secure data handling practices to meet compliance and privacy requirements (GDPR, CCPA), collaborating with security and legal teams.
  • Support and scale real-time or near-real-time data ingestion and processing solutions (Kafka, Kinesis, Pub/Sub, streaming frameworks) when low-latency analytics are required.
  • Partner with BI and analytics teams to design and deliver reports and dashboards (Tableau, Power BI, Looker) and to surface model outputs, ensuring metrics are consistent and well-documented.
  • Conduct capacity planning, cost forecasting and optimization of cloud data services (compute clusters, warehouse credits) and propose architecture improvements to reduce spend and improve scalability.
  • Implement data archival, retention policies, and lifecycle management for both raw and processed datasets in line with corporate policies and regulatory requirements.
  • Mentor junior analysts and engineers, conduct code and design reviews, and contribute to a culture of continuous improvement and engineering rigor.
  • Create clear, up-to-date technical documentation, runbooks, and onboarding materials for datasets, pipelines, and platform components to reduce knowledge silos.
  • Evaluate and onboard third-party data tools, SaaS vendors, and open-source projects, conducting POCs and vendor technical assessments consistent with enterprise architecture standards.
  • Participate in sprint planning and prioritized backlogs with product and engineering teams to align delivery cadence with business objectives and technical dependencies.
  • Troubleshoot complex data issues end-to-end — from source system behavior through ETL logic to BI consumption — communicating status and remediation plans to stakeholders in a timely manner.
  • Drive initiatives to improve data reliability and observability, including implementing data contracts, SLAs, and automatic reconciliation reports for critical data products.
  • Contribute to multi-team architecture decisions for data platform components (orchestration, warehouse, lakehouse, streaming) and help standardize patterns and best practices across the organization.
  • Support machine learning readiness by providing clean, feature-ready datasets, feature stores or transformation pipelines and by ensuring reproducibility and lineage for model inputs.
  • Lead efforts to refactor legacy ETL processes into modern, maintainable patterns (modular SQL, transformation frameworks, CI/CD-enabled pipelines) to reduce technical debt and increase velocity.

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.
  • Provide training and documentation for business users on data access patterns, metric definitions, and dashboard usage.
  • Assist in auditing datasets for compliance, PII exposure, and retention policy adherence.
  • Work with cross-functional teams to prototype data-driven products and proof-of-concepts that accelerate time-to-value.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: complex joins, window functions, CTEs, query performance tuning and profiling for warehouse platforms (Snowflake, BigQuery, Redshift).
  • Scripting and programming: Python (Pandas, SQLAlchemy), and familiarity with development best practices, unit testing and packaging.
  • ETL/ELT & Orchestration: hands-on experience with tools like Airflow, dbt, Talend, Informatica or equivalent pipeline frameworks.
  • Cloud Data Platforms: operational knowledge of Snowflake, BigQuery, Amazon Redshift, Databricks or a similar cloud data warehouse/lakehouse.
  • Data Modeling: dimensional modeling, normalization, denormalization strategies, and building consistent canonical data models.
  • Data Formats and Storage: working knowledge of Parquet, Avro, ORC, JSON, columnar storage and S3/GCS/Azure Blob patterns.
  • Streaming and Messaging: experience with Kafka, Kinesis, or Pub/Sub for event-driven architectures (preferred).
  • Observability and Monitoring: experience with pipeline monitoring, logging, alerting and SLA management (Prometheus, Grafana, DataDog, Sentry).
  • Metadata & Governance: implementing data cataloging, lineage tools and governance processes; knowledge of privacy regulations (GDPR, CCPA).
  • BI and Visualization: experience producing and supporting dashboards in Tableau, Power BI, Looker, or other analytics tools.
  • Version Control & CI/CD: Git, automated testing frameworks, and deployment pipelines for data code.
  • Performance & Cost Optimization: tuning queries, partitioning strategies, and optimizing cloud spend.
  • REST APIs and Data Integration: extracting data from APIs, using OAuth, handling pagination, rate limits, and data contracts.
  • Containerization & DevOps basics: familiarity with Docker, Kubernetes or comparable container orchestration for deployment scenarios (nice to have).

Soft Skills

  • Strong stakeholder management: ability to gather ambiguous requirements from business partners and translate them into technical deliverables.
  • Effective communication: explain technical trade-offs clearly to technical and non-technical audiences, produce clear documentation and runbooks.
  • Analytical problem solving: methodical troubleshooting and root cause analysis across large, distributed systems.
  • Prioritization and organization: manage competing requests and SLAs while delivering reliable data products.
  • Collaboration and mentoring: work across teams, mentor junior engineers, and contribute to a knowledge-sharing culture.
  • Adaptability and continuous learning: keep up with evolving cloud data technologies, frameworks and best practices.
  • Attention to detail with a focus on data quality, reproducibility and auditability.
  • Customer-centric mindset: think from the perspective of internal data consumers to deliver practical, usable solutions.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Data Science, Computer Science, Analytics, Applied Statistics, or an MBA with technical coursework (preferred but not required).

Relevant Fields of Study:

  • Computer Science
  • Data Science / Applied Statistics
  • Information Systems / IT
  • Mathematics / Applied Mathematics
  • Engineering (Electrical, Software)
  • Business Analytics

Experience Requirements

Typical Experience Range:

  • 2–5 years of hands-on experience building data pipelines, supporting analytics platforms, or performing data engineering work in a production environment.

Preferred:

  • 4–8+ years with demonstrable experience on cloud data platforms (Snowflake/BigQuery/Redshift), modern ETL/ELT tooling (dbt, Airflow), and a portfolio of delivered analytics or data infrastructure projects.
  • Experience working in cross-functional agile teams, with exposure to data governance, security, and compliance programs.
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