Data Analyst - Python/SQL

Job Summary:

We are looking for a motivated and skilled Analytics Engineer / Data Analyst with strong expertise in data modeling, ETL pipeline development, and analytics.

The ideal candidate will combine technical proficiency in data engineering and cloud platforms with strong business acumen, enabling data-driven insights and supporting decision-making across teams.

You will work closely with data scientists, business analysts, and stakeholders to deliver scalable, reliable, and high-quality data solutions.

Key Responsibilities:

- Design and implement scalable data models using star schema, snowflake schema, and dimensional modeling approaches.

- Build analytical datasets to support reporting, dashboards, and advanced analytics.

- Collaborate with business teams to understand metrics, KPIs, and reporting requirements.

- Develop and maintain ETL/ELT pipelines using Apache Spark, PySpark, or similar frameworks.

- Implement real-time streaming pipelines; experience with Apache Pulsar or Kafka is a plus.

- Ensure data reliability, consistency, and performance in pipelines.

- Optimize pipelines for latency, throughput, and cost-efficiency.

- Write production-grade Python scripts for data transformations, cleaning, and processing.

- Design and optimize SQL queries for large-scale relational databases.

- Perform exploratory data analysis to identify trends, anomalies, and insights.

- Use workflow orchestration tools such as Airflow, DBT, Prefect, or Luigi to schedule and monitor pipelines.

- Ensure observability, monitoring, and alerting for data pipelines.

- Automate repetitive data tasks and workflows.

- Work with cloud-native data warehouses/lakes such as:

Redshift, BigQuery, Snowflake, Databricks, Azure Synapse

Object storage: AWS S3, Azure Blob Storage, GCS

Manage operational data using relational databases like PostgreSQL, MySQL.

Optimize storage, query performance, and cost-efficiency on cloud platforms.

Implement data validation, testing, and quality checks across pipelines.

Maintain documentation, lineage, and metadata management for datasets.

Collaborate with stakeholders to ensure compliance with data governance and security policies.

Collaborate with data engineers, data scientists, analysts, and business teams.

Translate business requirements into technical solutions and scalable data models.

Present findings and insights to technical and non-technical stakeholders.

Required Skills & Technical Expertise

- 46 years of experience in data analytics, data engineering, or analytics engineering.

- Strong programming skills in Python (including Pandas, NumPy, PySpark).

- Advanced SQL skills for querying, data transformations, and optimization.

- Experience with ETL/ELT frameworks and data pipeline orchestration (Airflow, DBT, Prefect).

- Familiarity with cloud data platforms: Redshift, BigQuery, Snowflake, Databricks.

- Experience with streaming data platforms like Apache Pulsar, Kafka, or Kinesis is a plus.

- Understanding of dimensional modeling, star/snowflake schema, and data warehousing concepts.

- Familiarity with data quality frameworks, testing, and observability