Data Analyst

Data Analyst Mumbai (on-site) - LowerParel

About HoABL

We are The House of Abhinandan Lodha. We are a consumer tech brand that disrupts by leveraging technology to make land more accessible, flexible and secure. We are breaking old traditions and bringing land ownership into the 21st century, making an age-old asset young again for now and for generations to come. We are on a mission to create intergenerational wealth for our consumers. We believe in complete transparency in every process, whether we are dealing with a homeowner, an investor or a real estate professional. For HoABL, it is not just a piece of land we believe in the Peace of Land. Our curated developments are self-sustaining ecosystems built and maintained using sustainable forms of development, with transparency and fairness at the core of all our dealings with local people and resources. Our vision is to make land amazing again by democratising its ownership making it a younger, nimbler and more viable asset for Indians everywhere. We use technology to reinvent the way land is experienced: by digitising its ownership, applying complete transparency in all aspects, and making investments simpler and more accessible.

Responsibilities

• Data aggregation and processing. Aggregate, clean, standardise, deduplicate and organise data from a wide range of internal and third-party sources, ensuring data accuracy and integrity at scale.

• Pipeline ownership. Run the existing data pipelines and recurring outputs to schedule with no drop in turnaround, and consolidate them into automated, monitored, orchestrated workflows so new requirements can be served repeatably.

• Data analysis. Analyse large volumes of data to identify patterns, trends and correlations, and turn them into insight that shapes sales targeting, marketing and channel-partner strategy.

• Segmentation and audience building. Build and maintain the segments and lead files that business teams work from, including within our customer data platform.

• Forecasting and modelling. Apply statistical techniques and predictive modelling including lead scoring and propensity to forecast trends and prioritise effort towards the highest-intent opportunities.

• Performance tracking. Develop and maintain KPIs to monitor performance, highlighting areas of improvement and potential risk.

• Reporting and visualisation. Prepare clear reports and dashboards for management and business teams, and replace manual MIS pulls with automated, self-serve reporting.

• Data quality assurance. Implement automated data-quality checks, identify discrepancies and work with relevant teams to resolve data issues before they reach stakeholders.

• Master data and documentation. Own the master data files and standard taxonomies other teams build on, and document the data estate so delivery never depends on a single person.

• Cross-functional collaboration. Work with sales, marketing, finance and operations to gather data requirements and support their analytical needs.

• Stay updated. Keep abreast of industry trends, market conditions and emerging data technologies to continuously enhance our analytics capability.

Requirements

• Bachelor's degree in a relevant field (Data Science, Statistics, Computer Science or a related discipline).
• 23 years of experience in data analytics, data engineering or data science, with hands-on ownership of production data work.
• Advanced Python mandatory. Daily, hands-on use of pandas / NumPy with vectorised logic; comfortable with datasets well beyond spreadsheet and memory-comfortable limits; sound reasoning on data types, keys, joins and chunking. Production pipelines, not just analysis notebooks.
• Advanced SQL — mandatory. Window functions, complex multi-table joins, deduplication and aggregation logic, CTEs and query optimisation.
• Scale. Demonstrable experience processing tens to hundreds of millions of rows, with a clear understanding of what breaks at that scale and why.
• Data quality discipline. A habit of building validation into every deliverable: row-count reconciliation, key type sanity and duplicate accounting.
• Visualisation. Experience with data visualisation and dashboarding tools (Power BI, Tableau or similar) to create meaningful reports.
• Communication. Strong communication and presentation skills; able to translate complex data into actionable insight for both technical and non-technical stakeholders, and to write documentation others can follow.
• Working style. Detail-oriented, accuracy-focused, and able to work both independently and collaboratively in a fast-paced environment.
• Experience of working with Salesforce Data Cloud preferred.