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Key Responsibilities and Required Skills for Analytics Engineer

💰 $ - $

🎯 Role Definition

An Analytics Engineer is a hybrid data professional who combines software engineering practices with analytics and business domain knowledge to design, build, test, and maintain robust data pipelines, models, and business-facing analytics. The role owns end-to-end transformation of raw data into trusted, documented, and performant semantic layers and datasets used by analysts, data scientists, and business stakeholders. Analytics Engineers focus on data modeling, modular SQL, ETL/ELT orchestration, data testing and observability, CI/CD for analytics, and close partnership with product and business teams to translate requirements into repeatable, scalable analytics solutions.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning to production-grade data engineering and modeling.
  • BI Developer experienced in dashboarding and data transformation.
  • Junior Data Engineer who has worked on pipeline tooling and SQL-heavy ETL.

Advancement To:

  • Senior Analytics Engineer / Lead Analytics Engineer
  • Analytics Engineering Manager or Data Engineering Manager
  • Head of Analytics / Director of Data Platforms

Lateral Moves:

  • Data Engineer (specializing in upstream ingestion and infrastructure)
  • Data Scientist (focused on advanced modeling and experimentation)
  • BI/Product Analytics Manager (managing analytics deliverables)

Core Responsibilities

Primary Functions

  • Design, develop, and maintain production-grade ETL/ELT data pipelines using modern data stack technologies (dbt, Airflow, Dagster, or native cloud orchestration) to transform raw event and transactional data into curated, documented analytics models and marts.
  • Build and maintain star/snowflake schemas, semantic layers, and dimensional models that support fast and accurate business reporting, self-service BI, and downstream machine learning use cases.
  • Develop modular, testable SQL and transformations (dbt models, macros, and hooks) that follow best practices for reusability, documentation, and maintainability; implement version control (Git) and branching strategies for analytics code.
  • Implement rigorous data quality testing and observability (dbt tests, Great Expectations, Monte Carlo, or bespoke test suites) to detect anomalies, ensure schema and KPI stability, and manage data SLAs.
  • Translate business requirements and KPIs into data models, source-to-target mappings, and clear acceptance criteria; partner with product managers, analysts, and stakeholders to scope analytics deliverables and success metrics.
  • Optimize data models and queries for performance on cloud data warehouses (Snowflake, BigQuery, Redshift), including partitioning, clustering, materializations, and cost-aware optimization to reduce runtime and warehouse spend.
  • Create and maintain documentation, data dictionaries, lineage diagrams, and onboarding guides to ensure dataset discoverability and trusted consumption by engineers, analysts, and business users.
  • Maintain CI/CD pipelines for analytics code, including automated testing, linting, model builds, and deployment to production environments, ensuring repeatable and safe changes to analytics artifacts.
  • Implement semantic layers and trusted metrics in BI tools (Looker, Tableau, Power BI) or metrics stores to enforce single sources of truth and reduce reporting drift across dashboards and reports.
  • Instrument and maintain data ingestion processes from diverse sources (databases, event streams, APIs, cloud storage), validate schema evolution, and design fallback and backfill strategies for upstream changes.
  • Collaborate with data platform and infrastructure teams to improve data access patterns, governance, roles/permissions, and cost allocation while ensuring security and compliance standards are met.
  • Partner with analysts and data scientists to prototype and productionize features, cohorts, and experimental analysis models, ensuring reproducibility and clear lineage from raw events to derived features.
  • Conduct root-cause analysis for broken reports or KPIs, rapidly identify source-of-truth discrepancies, and remediate upstream or downstream issues with robust fix and prevention plans.
  • Provide technical leadership and best practices coaching for analytics engineering standards, SQL style guides, naming conventions, and code review processes across the data organization.
  • Plan and execute dataset migrations, refactors, or large-scale data model redesigns, including coordinated stakeholder communication, migration scripts, and validation procedures to minimize consumer impact.
  • Monitor pipeline health, SLOs, and error budgets; escalate and remediate production incidents, and coordinate postmortems with actionable improvements to prevent recurrence.
  • Design and implement templated, reusable data models and macros to accelerate business domain onboarding (sales, marketing, finance, product) and standardize metric calculations across teams.
  • Evaluate and recommend analytics tools, data catalog and lineage solutions, and cost/performance trade-offs to evolve the analytics stack in alignment with product and platform roadmaps.
  • Enable and support self-service analytics by training business users, producing well-documented curated datasets, and collaborating with analytics translators to democratize data access.
  • Lead cross-functional projects to instrument tracking, improve data completeness, and reduce measurement gaps by collaborating with engineering, QA, and product teams on event design and tracking plans.
  • Implement role-based access and data governance best practices for curated analytics datasets, ensuring sensitive data is flagged, masked, or restricted per company policy and compliance requirements.
  • Contribute to sprint planning, roadmap prioritization, and capacity estimations for analytics initiatives; balance technical debt, refactors, and new feature requests with business priorities.

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 proficiency with experience writing complex, performant queries, window functions, CTEs, and set operations for analytics-grade modeling.
  • Hands-on experience with dbt (data build tool): model development, macros, tests, docs, snapshots, and dbt Cloud or dbt Core workflows.
  • Familiarity with cloud data warehouses and big data platforms such as Snowflake, Google BigQuery, Amazon Redshift, or Databricks.
  • Experience designing dimensional models, star/snowflake schemas, and semantic layers for consistent metric calculation and self-service BI.
  • Knowledge of ETL/ELT orchestration and scheduling tools: Airflow, Dagster, Prefect, or managed cloud schedulers; understanding of task dependencies and monitoring.
  • Proficiency in a scripting or programming language commonly used in analytics engineering (Python, Bash) for automation, ingestion scripts, and test harnesses.
  • Experience implementing data quality, testing frameworks, and observability tooling (dbt tests, Great Expectations, Monte Carlo, Datafold) and setting data SLAs.
  • Familiarity with BI tools and semantic layer platforms (Looker, Looker Studio, Tableau, Power BI, Hex) and how to expose curated datasets to analysts and stakeholders.
  • Version control and CI/CD experience (Git, GitHub/GitLab, CI pipelines) for analytics code, automated builds and deployments, and pull request workflows.
  • Understanding of data governance, access controls, masking, PII handling, and security best practices in cloud environments.
  • Knowledge of performance tuning and cost optimization techniques for cloud warehouses (clustering, partitioning, materialized views, cost-aware query design).
  • Experience with event-driven data and streaming ingestion patterns (Kafka, Kinesis, Pub/Sub) is a plus.
  • Familiarity with feature stores, online/offline splits, and productionization of features for ML consumers is beneficial.
  • Ability to read and interpret application logs, API outputs, and source database schemas to design robust ingestion and transformation logic.

Soft Skills

  • Strong stakeholder management with the ability to translate ambiguous business questions into concrete, testable analytics deliverables and acceptance criteria.
  • Excellent communication and documentation skills — producing clear README, data dictionaries, onboarding guides, and status updates for cross-functional teams.
  • Problem-solving mindset with attention to detail and a bias for operational excellence and long-term maintainability over quick fixes.
  • Collaborative approach: works closely with engineers, analysts, product managers, and QA to design reliable analytics solutions.
  • Prioritization and time management: able to balance urgent incident work with roadmap-driven refactors and new feature development.
  • Mentoring and knowledge-sharing orientation: coach junior engineers and analysts on tooling, SQL best practices, and analytics engineering principles.
  • Adaptability to evolving toolchains and commitment to continuous learning of emerging data stack technologies.
  • Analytical curiosity and product focus: cares about the business impact of metrics and uses data to drive decisions and improvements.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field — or equivalent practical experience.

Preferred Education:

  • Master's degree in a quantitative discipline, Data Engineering, or Data Science; relevant professional certifications (dbt Fundamentals, Snowflake SnowPro, Google Cloud Professional Data Engineer) are a plus.

Relevant Fields of Study:

  • Computer Science / Software Engineering
  • Data Science / Statistics / Mathematics
  • Economics / Finance with quantitative coursework
  • Information Systems / Engineering

Experience Requirements

Typical Experience Range:

  • 3–7 years of experience in analytics, BI, or data engineering roles with a demonstrated track record delivering production analytics pipelines and curated datasets.

Preferred:

  • 5+ years experience including hands-on work with dbt and cloud data warehouses (Snowflake, BigQuery, Redshift), proven impact building semantic layers and operationalizing analytics workflows, and experience mentoring or leading small analytics engineering teams.
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