Key Responsibilities and Required Skills for Data Analytics Engineer

🎯 Role Definition

The Data Analytics Engineer is a cross-functional technical partner who builds, tests, and maintains scalable analytics pipelines, creates governed data models and semantic layers, and delivers high-quality data products that enable self-service analytics and operational insights. This role blends strong software engineering practices (CI/CD, observability, testing) with analytics-first thinking (dimensional modeling, metrics, BI consumption), working closely with data scientists, product managers, and business stakeholders to translate questions into reliable data solutions.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst transitioning into engineering-focused work (3+ months of pipeline ownership)
Business Intelligence Developer with experience building data models
Junior Data Engineer contributing to ETL/ELT and reporting

Advancement To:

Senior Data Analytics Engineer
Analytics Engineering Lead / Manager
Data Platform Engineer or Head of Analytics Engineering

Lateral Moves:

Data Scientist (with focus on modeling and productionization)
Business Intelligence Manager
Data Product Manager

Core Responsibilities

Primary Functions

Design, implement, and maintain robust ETL/ELT pipelines that transform raw event and transactional data into clean, analytics-ready datasets, ensuring reproducibility, scalability, and observability across environments.
Develop and maintain dimensional data models, canonical tables, and semantic layers (metrics, business views) that enable consistent KPI definitions and self-service BI across Tableau, Looker, Power BI, or equivalent tools.
Author modular, tested dbt models (or comparable transformations) with clear documentation, version control, and automated testing to enforce data quality and lineage for downstream consumers.
Build and optimize SQL-based data extraction, transformation, and aggregation logic for performance and cost-efficiency on cloud data warehouses such as Snowflake, BigQuery, or Redshift.
Partner with product managers and business stakeholders to translate analytic requirements into technical specifications, data schemas, and prioritized backlog items that deliver measurable business outcomes.
Implement and monitor data quality frameworks (unit tests, schema checks, anomaly detection) and respond to incidents with root-cause analysis and remediation plans to minimize data downtime.
Instrument and maintain data pipeline orchestration (Airflow, Dagster, Prefect) and CI/CD pipelines for analytics code, ensuring reproducible deploys, rollback strategies, and environment parity.
Define and enforce data governance practices, including access controls, data cataloging, metadata management, and lineage tracking to support compliance and secure, discoverable data assets.
Create repeatable ingestion patterns for batch and streaming sources (Kafka, Pub/Sub, Kinesis), handling schema evolution, late-arriving data, and backfill strategies to keep datasets accurate and timely.
Profile, monitor, and optimize dataset freshness, row-level costing, and query performance through indexing, partitioning, clustering, and materialization strategies to reduce latency and compute spend.
Design and build analytics APIs, aggregated views, or data marts that power product features, dashboards, and operational workflows while minimizing duplication of logic across teams.
Implement observability and alerting for data pipelines and downstream reporting (SLAs, data health dashboards) to provide early warning of data quality degradation and pipeline failures.
Collaborate with data scientists to productionize models and feature stores while ensuring traceability and reproducibility between training data and production features.
Conduct regular data lineage reviews and maintain clear documentation that describes source systems, transformation logic, and business definitions to promote cross-team trust and onboarding velocity.
Lead or contribute to cross-functional analytics projects, coordinating release plans, dependency management, and stakeholder communication to align technical delivery with business timelines.
Drive continuous improvement of analytics engineering practices by introducing new automation, frameworks, and best practices for testing, code review, and incremental adoption.
Mentor junior analytics engineers and analysts on querying best practices, modular modeling, version control workflows, and interpretation of key metrics.
Evaluate and recommend cloud services, data warehouse configurations, and third-party tools that optimize for reliability, scalability, and cost across analytics workloads.
Run A/B test instrumentation audits and ensure experiment datasets are reliable and aligned with product measurement plans and statistical needs.
Perform deep-dive analyses to debug complex data discrepancies, produce reproducible root-cause notebooks or playbooks, and implement permanent fixes to avoid regressions.
Translate complex technical trade-offs into clear guidance for product and business stakeholders, balancing accuracy, latency, and cost considerations.
Build and maintain transformation frameworks and templates that accelerate onboarding of new data sources and reduce time-to-insight for new business initiatives.
Participate in capacity planning and workload forecasting to maintain expected service levels during traffic growth or seasonal spikes.
Advocate for a data-driven culture by delivering internal training sessions, creating how-to guides for data consumers, and curating a catalog of trusted analytics assets.

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 expertise: complex window functions, CTEs, performance tuning, and query plan interpretation for analytic workloads.
Proficient in Python (or Scala) for ETL/ELT, testing, orchestration scripts, and lightweight data processing tasks.
Experience with analytics transformation frameworks: dbt, SQL-based models, templating, and testing best practices.
Deep familiarity with cloud data warehouses and storage solutions: Snowflake, BigQuery, Redshift, S3, GCS, including cost and performance trade-offs.
Hands-on experience with workflow orchestration tools: Airflow, Dagster, Prefect, or equivalent, including defining DAGs, SLA policies, and retry strategies.
Knowledge of streaming ingestion and processing patterns using Kafka, Pub/Sub, Kinesis, or streaming frameworks.
Experience implementing data quality frameworks: Great Expectations, custom assertions, monitoring dashboards, and alerting.
Proficiency with version control (Git), CI/CD pipelines for data code, and automated testing frameworks for analytics.
Familiarity with BI tools and dashboarding best practices: Looker, Tableau, Power BI, or Metabase, including semantic modeling and performance optimization.
Understanding of data modeling concepts: star schema, slowly changing dimensions, fact/dimension separation, and canonical modeling patterns.
Experience with metadata, cataloging, and lineage tools (e.g., Amundsen, DataHub, Collibra) and implementing RBAC for datasets.
Basic statistics and experiment measurement knowledge (A/B testing fundamentals, significance, and power considerations).
Experience with monitoring, logging, and observability stacks as they pertain to data pipelines and analytics (Prometheus, Grafana, Datadog).
Familiarity with data privacy, security, and compliance concepts (PII handling, GDPR/CCPA awareness) within analytics contexts.

Soft Skills

Strong stakeholder management: able to gather requirements, set expectations, and communicate trade-offs to non-technical audiences.
Excellent written documentation skills to create clear data contracts, runbooks, and onboarding materials for analytics consumers.
Problem-solving mindset with a focus on root cause analysis and lasting remediation rather than short-term fixes.
Collaboration-first approach: comfortable working in cross-functional teams with product, engineering, and business partners.
Prioritization and time management to balance maintenance, incident response, and new feature delivery.
Curiosity and business acumen to translate ambiguous business questions into measurable analytics outcomes.
Mentoring and teaching aptitude to uplift junior teammates and promote consistent best practices.
Adaptability to evolving tech stacks, data volumes, and changing product priorities.
Attention to detail in schema design, documentation, and tests to prevent downstream reporting errors.
Effective presentation skills for delivering insights, demos, and technical proposals to stakeholders and leadership.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Data Science, Statistics, Information Systems, Engineering, Business Analytics, or a related technical field, or equivalent practical experience.

Preferred Education:

Master's degree in Data Science, Computer Science, Analytics, or related field, or professional certifications in cloud/data engineering, dbt, or BI platforms.

Relevant Fields of Study:

Computer Science or Software Engineering
Data Science, Statistics, or Applied Mathematics
Information Systems or Business Analytics
Electrical Engineering, Industrial Engineering (with analytics coursework)

Experience Requirements

Typical Experience Range:

3 to 7 years building analytics pipelines, data models, or BI systems; or equivalent industry experience.

Preferred:

5+ years with hands-on ownership of analytics transformation pipelines, cloud data warehouses, and BI semantic layers; demonstrated cross-functional delivery and production incident management.